The solution, which can run on commodity hardware or in the public cloud, seeks to help enterprises vexed by unstructured data. The company says that Scale Anywhere uses a unified approach to improve efficiency, security, and business agility.

In a briefing with ZK Research, Qumulo CTO Kiran Bhageshpur gave me some background on the platform. “We look at this as being the third era of unstructured data,” he told me. “The first era was NetApp with scale-up, dual controller architectures, and millions of files. It was really a sort of analysis box, if you will. The second era was Isilon, then EMC Isilon, now Dell EMC Isilon, which is scale-out storage, hardware appliances, on-premises, lots of them together to form large single volumes.”

Cloud-Based Qumulo Competes with Legacy Systems

Kiran said that Qumulo started in the cloud computing era, looked at the world, and realized it was no longer the scale-up or scale-out era.

“This is the scale-anywhere era of large-scale data,” he said. “It’s not only lots of data in the enterprise data center—there is incredible growth in the cloud and out at the edge. And Qumulo, with a pure software solution, can now present a solution for all of this data—cloud, on-premises, and the edge in one consistent way.”

Qumulo says that Scale Anywhere introduces a way for enterprises to use on-premises storage in a similar way to cloud storage.

The company jointly developed Azure Native Qumulo (ANQ) with Microsoft. This cloud-native enterprise file system helps eliminate the tradeoffs that often come with balancing scale, economics, and performance.

Qumulo is trumpeting a number of advantages to the approach, including:

• Affordability: Qumulo says that ANQ is about 80% cheaper than competitive offerings and compares well to the costs of traditional on-premises storage.
• Elasticity: Qumulo says that ANQ separates the scalability of capacity and performance so they can operate independently.
• Cloud configurable: Qumulo says enterprises can use the Azure service portal to configure and deploy ANQ quickly.
• Data services: Qumulo says that ANQ provides several data services, including quotas, snapshots, multi-protocol access, enterprise security integrations, and real-time data analytics.

The company also announced Qumulo Global Namespace (Q-GNS), which acts as a unified data plane for unstructured data.

“This is the core feature of the underlying Qumulo file system, and it allows the customer to access remote data on a remote Qumulo cluster as if it were local,” Kiran told me. “Think of two, three, or four Qumulo clusters talking to each other. You can connect to the local one. And as long as it’s configured correctly, you can access data on a Qumulo cluster in the cloud or on-premises halfway across the world, and it feels as though it were local.”

In the announcement, JD Whitlock, CIO of Dayton Children’s Hospital, said that his hospital uses Q-GNS.

“We are rapidly adopting cloud to store our long-term radiology images while keeping new images on-premises,” Whitlock said. “Qumulo’s Global Namespace makes it easy to bring our file-based workloads to the cloud without refactoring any applications.”

Also see: Top Cloud Service Providers and Companies

Bottom Line: Storage for the Cloud Era

Legacy storage vendors like Dell EMC view data storage as an entitlement and haven’t delivered innovation in years. Many believe storage to be a commodity with little room for new features and functions, but that’s not true. The announcement by Qumulo modernizes storage for the cloud era. The company has a lot of work ahead of it, but the approach is innovative and might just make a dent in the defenses of the legacy players.

Read next: Top Digital Transformation Companies

The post Qumulo’s New Scale Anywhere Platform Aims to Modernize Data Storage appeared first on eWEEK.

Both of these application and service suites are in heavy demand, as organizations seek to harness real-time repositories of big data for various enterprise use cases, including artificial intelligence and machine learning model development and deployment.

When choosing between two of the most highly regarded data platforms on the market, users often have difficulty differentiating between Cognos and Power BI and weighing each of the platform’s pros and cons. In this in-depth comparison guide, we’ll compare these two platforms across a variety of qualities and variables to assess where their strengths lie.

But first, here’s a glance at the areas where each tool excels most:

• Cognos Analytics: Best for advanced data analytics and on-premises deployment. Compared to Power BI, Cognos is particularly effective for advanced enterprise data analytics use cases that require more administrative controls over security and governance. Additionally, it is more reliable when it comes to processing large quantities of data quickly and accurately.
• Power BI: Best for affordable, easy-to-use, integrable BI technology in the cloud. Compared to Cognos Analytics, Power BI is much more versatile and will fit into the budget, skill sets, and other requirements of a wider range of teams. Most significant, this platform offers free access versions that are great for teams that are just getting started with this type of technology.

Cognos vs. Power BI at a Glance

What Is Cognos?

Cognos Analytics is a business intelligence suite of solutions from IBM that combines AI-driven assistance, advanced reporting and analytics, and other tools to support various enterprise data management requirements. The platform is available both in the cloud and on demand for on-premises and custom enterprise network configurations.

With its range of features, Cognos enables users to connect, verify, and combine data and offers plenty of dashboard and visualization options. Cognos is particularly good at pulling and analyzing corporate data, providing detailed reports, and assisting in corporate governance. It is built on a strong data science foundation and is supported by heavy-duty analytics and recommendations, courtesy of IBM Watson.

Also see: Top Business Intelligence Software

Key Features of Cognos

• AI-driven insights: The platform benefits from veteran AI support in the form of Watson, which helps with data visualization design, dashboard builds, forecasting, and data explainability. This is particularly helpful for users with limited data science and coding experience who need to pull in-depth analyses from complex datasets.
• Data democratization through natural language: Advanced natural language capabilities make it possible for citizen data scientists and less-experienced tech professionals to create accurate and detailed data visualizations.
• Advanced reporting and dashboarding: Multi-user reports and dashboards, personalized report generation, AI-powered dashboard design, and easy shareability make this a great platform for organizations that require different levels of data visibility and granularity for different stakeholders.
• Automation and governance: Extensive automation and governance capabilities help power users scale their operations without compromising data security. The platform’s robust governance and security features are important to highly regulated businesses and large enterprises in particular.

Pros

• The platform is well integrated with other business tools, like Slack and various email inboxes, making it easier to collaborate and share insights across a team.
• Its AI assistant works well for a variety of data analytics and management tasks, even for users with no data science experience, because of its natural language interface.
• Cognos comes with flexible deployment options, including on-demand cloud, hosted cloud, and client hosting for either on-premises or IaaS infrastructure.

Cons

• The platform is not particularly mobile-friendly compared to similar competitors.
• While a range of visuals are available on the platform, many user reviews indicate that the platform’s visuals are limited and not very customizable.
• Depending on your exact requirements, Cognos Analytics can become quite expensive, especially if you have a high user count or require more advanced features like security and user management.

What Is Power BI?

Microsoft Power BI is a business intelligence and data visualization software solution that acts as one part of the Microsoft Power Platform. Because of its unification with other Power Platform products like Power Automate, Power Apps, and Power Pages, this BI tool gives users diverse low-code and AI-driven operations for more streamlined data analytics and management. Additional integrations with the likes of Microsoft 365, Teams, Azure, and SharePoint are a major selling point, as many business users are already highly invested in these business applications and are familiar with the Microsoft approach to UX/UI.

Specific to analytics functions, Power BI focuses most heavily on data preparation, data discovery, dashboards, and data visualization. Its core features enable users to take visualizations to the next level and empower them to make data-driven decisions, collaborate on reports, and share insights across popular applications. They can also create and modify data reports and dashboards easily and share them securely across applications.

Key Features of Power BI

• Rapidly expanding AI analytics: AI-powered data analysis and report creation have already been established in this platform, but recently, the generative AI Copilot tool has also come into preview for Power BI. This expands the platform’s ability to create reports more quickly, summarize and explain data in real time, and generate DAX calculations.
• CRM integration: Power BI integrates relatively well with Microsoft Dynamics CRM, which makes it a great option for in-depth marketing and sales analytics tasks. Many similar data platforms do not offer such smooth CRM integration capabilities.
• Embedded and integrated analytics: The platform is available in many different formats, including as an embedded analytics product. This makes it possible for users of other Microsoft products to easily incorporate advanced analytics into their other most-used Microsoft products. You can also embed detailed reports in other apps for key stakeholders who need information in a digestible format.
• Comprehensive visualizations: Adjustable dashboards, AI-generated and templated reports, and a variety of self-service features enable users to set up visuals that can be alphanumeric, graphical, or even include geographic regions and maps. Power BI’s many native visualization options mean users won’t have to spend too much time trying to custom-fit their dashboards and reports to their company’s specific needs.

Pros

• Power BI is one of the more mobile-friendly data platforms on the market today.
• In addition to its user-friendly and easy-to-learn interface, Microsoft offers a range of learning resources and is praised for its customer support.
• Its AI-powered capabilities continue to grow, especially through the company’s close partnership with OpenAI.

Cons

• Some users have commented on the tool’s outdated interface and how data updates, especially for large amounts of data, can be slow and buggy.
• The platform, especially the Desktop tool, uses a lot of processing power, which can occasionally lead to slower load times and platform crashes.
• Shareability and collaboration features are incredibly limited outside of its highest paid plan tier.

Best for Core Features: It Depends

It’s a toss-up when it comes to the core features Cognos Analytics and Power BI bring to the table.

Microsoft Power BI’s core features include a capable mobile interface, AI-powered analytics, democratized report-building tools and templates, and intuitive integrations with other Microsoft products.

IBM Cognos Analytics’ core features include a web-based report authoring tool, natural-language and AI-powered analytics, customizable dashboards, and security and access management capabilities. Both tools offer a variety of core features that work to balance robustness and accessibility for analytics tasks.

To truly differentiate itself, Microsoft consistently releases updates to its cloud-based services, with notable updates and feature additions over the past couple of years including AI-infused experiences, smart narratives (NLG), and anomaly detection capabilities. Additionally, a Power BI Premium version enables multi-geography capabilities and the ability to deploy capacity to one of dozens of data centers worldwide.

On the other hand, IBM has done extensive work to update the Cognos home screen, simplifying the user experience and giving it a more modern look and feel. Onboarding for new users has been streamlined with video tutorials and accelerator content organized in an easy-to-consume format. Additionally, improved search capabilities and enhancements to the Cognos AI Assistant and Watson features help generate dashboards automatically, recommend the best visualizations, and suggest questions to ask — via natural language query — to dive deeper into data exploration.

Taking these core capabilities and recent additions into account, which product wins on core features? Well, it depends on the user’s needs. For most users, Power BI is a stronger option for general cloud and mobility features, while Cognos takes the lead on advanced reporting, data governance, and security.

Also see: Top Dashboard Software & Tools

Best for Ease of Use and Implementation: Power BI

Although it’s close, new users of these tools seem to find Power BI a little easier to use and set up than Cognos Analytics.

As the complexity of your requirements rises, though, the Power BI platform grows more difficult to navigate. Users who are familiar with Microsoft tools will be in the best position to use the platform seamlessly, as they can take advantage of skills from applications they already use, such as Microsoft Excel, to move from building to analyzing to presenting with less data preparation. Further, all Power BI users have access to plenty of free learning opportunities that enable them to rapidly start building reports and dashboards.

Cognos, on the other hand, has a more challenging learning curve, but IBM has been working on this, particularly with recent user interface updates, guided UI for dashboard builds, and assistive AI. The tool’s AI-powered and Watson-backed analytics capabilities in particular lower the barrier of entry to employing advanced data science techniques.

The conclusion: Power BI wins on broad usage by a non-technical audience, whereas IBM has the edge with technical users and continues to improve its stance with less-technical users. Overall, Power BI wins in this category due to generally more favorable user reviews and commentary about ease of use.

Also see: Top AI Software

Best for Advanced Analytics Capabilities: Cognos

Cognos Analytics surpasses Power BI for its variety of in-depth and advanced analytics operations.

Cognos integrates nicely with other IBM solutions, like the IBM Cloud Pak for Data platform, which extends the tool’s already robust data analysis and management features. It also brings together a multitude of data sources as well as an AI Assistant tool that can communicate in plain English, sharing fast recommendations that are easy to understand and implement. Additionally, the platform generates an extensive collection of visualizations. This includes geospatial mapping and dashboards that enable the user to drill down, rise, or move horizontally through visuals that are updated in real time.

Recent updates to Cognos’s analytical capabilities include a display of narrative insights in dashboard visualizations to show meaningful aspects of a chart’s data in natural language, the ability to specify the zoom level for dashboard viewing and horizontal scrolling in visualizations, as well as other visualization improvements.

On the modeling side of Cognos, data modules can be dynamically redirected to different data server connections, schemas, or catalogs at run-time. Further, the Convert and Relink options are available for all types of referenced tables, and better web-based modeling has been added.

However, it’s important to note that Cognos still takes a comparatively rigid, templated approach to visualization, which makes custom configurations difficult or even impossible for certain use cases. Additionally, some users say it takes extensive technical aptitude to do more complex analysis.

Power BI’s strength is out-of-the-box analytics that doesn’t require extensive integration or data science smarts. It regularly adds to its feature set. More recently, it has added new features for embedded analytics that enable users to embed an interactive data exploration and report creation experience in applications such as Dynamics 365 and SharePoint.

For modeling, Microsoft has added two new statistical DAX functions, making it possible to simultaneously filter more than one table in a remote source group. It also offers an Optimize ribbon in Power BI Desktop to streamline the process of authoring reports (especially in DirectQuery mode) and more conveniently launch Performance Analyzer to analyze queries and generate report visuals. And while Copilot is still in preview at this time, this tool shows promise for advancing the platform’s advanced analytics capabilities without negatively impacting its ease of use.

In summary, Power BI is good at crunching and analyzing real-time data and continues to grow its capabilities, but Cognos Analytics maintains its edge, especially because Cognos can conduct far deeper analytics explorations on larger amounts of data without as many reported performance issues.

Also see: Data Analytics Trends

Best for Cloud Users: Power BI; Best for On-Prem Users: Cognos

Both platforms offer cloud and on-premises options for users, but each one has a clear niche: Power BI is most successful on the cloud, while Cognos has its roots in on-prem setups.

Power BI has a fully functional SaaS version running in Azure as well as an on-premises version in the form of Power BI Report Server. Power BI Desktop is also offered for free as a standalone personal analysis tool.

Although Power BI does offer on-prem capabilities, power users who are engaged in complex analysis of multiple on-premises data sources typically still need to download Power BI Desktop in addition to working with Power BI Report Server. The on-premises product is incredibly limited when it comes to dashboards, streaming analytics, natural language, and alerting.

Cognos also offers both cloud and on-premises versions, with on-demand, hosted, and flexible on-premises deployment options that support reporting, dashboarding, visualizations, alters and monitoring, AI, and security and user management, regardless of which deployment you choose. However, Cognos’ DNA is rooted in on-prem, so it lags behind Microsoft on cloud-based bells and whistles.

Therefore, Microsoft gets the nod for cloud analytics, and Cognos for on-prem, but both are capable of operating in either format.

Also see: Top Data Visualization Tools

Best for Integrations: It Depends

Both Cognos Analytics and Power BI offer a range of helpful data storage, SaaS, and operational tool integrations that users find helpful. Ultimately, neither tool wins this category because they each have different strengths here.

Microsoft offers an extensive array of integration options natively, as well as APIs and partnerships that help to make Power BI more extensible. Power BI is tightly embedded into much of the Microsoft ecosystem, which makes it ideally suited for current Azure, Dynamics, Microsoft 365, and other Microsoft customers. However, the company is facing some challenges when it comes to integrations beyond this ecosystem, and some user reviews have reflected frustrations with that challenge.

IBM Cognos connects to a large number of data sources, including spreadsheets. It is well integrated into several parts of the vast IBM portfolio. It integrates nicely, for example, with the IBM Cloud Pak for Data platform and more recently has added integration with Jupyter notebooks. This means users can create and upload notebooks into Cognos Analytics and work with Cognos Analytics data in a notebook using Python scripts. The platform also comes with useful third-party integrations and connectors for tools like Slack, which help to extend the tool’s collaborative usage capabilities.

This category is all about which platform and IT ecosystem you live within, so it’s hard to say which tool offers the best integration options for your needs. Those invested in Microsoft will enjoy tight integration within that sphere if they select Power BI. Similarly, those who are committed to all things IBM will enjoy the many ways IBM’s diverse product and service set fit with Cognos.

Also see: Digital Transformation Guide: Definition, Types & Strategy

Best for Pricing: Power BI

While Cognos Analytics offers some lower-level tool features at a low price point, Power BI offers more comprehensive and affordable entry-level packages to its users.

Microsoft is very good at keeping prices low as a tactic for growing market share. It offers a lot of features at a relatively low price. Power BI Pro, for example, costs approximately $10 per user per month, while the Premium plan is $20 per user per month. Free, somewhat limited versions of the platform are also available via Power BI Desktop and free Power BI accounts in Microsoft Fabric.

The bottom line for any rival is that it is hard to compete with Microsoft Power BI on price, especially because many of its most advanced features — including automated ML capabilities and AI-powered services — are available in affordable plan options.

IBM Cognos Analytics, on the other hand, has a reputation for being expensive. It is hard for IBM to compete with Power BI on price alone.

IBM Cognos Analytics pricing starts at $10 per user per month for on-demand cloud access and $5 per user per month for limited mobile user access to visuals and alerts on the cloud-hosted or client-hosted versions. For users who want more than viewer access and the most basic of capabilities, pricing can be anywhere from $40 to $450 per user per month.

Because of the major differences in what each product offers in its affordable plans, Microsoft wins on pricing.

Also see: Data Mining Techniques

Why Shouldn’t You Use Cognos or Power BI?

While both data and BI platforms offer extensive capabilities and useful features to users, it’s possible that these tools won’t meet your particular needs or align with industry-specific use cases in your field. If any of the following points are true for your business, you may want to consider an alternative to Cognos or Power BI:

Who Shouldn’t Use Cognos

The following types of users and companies should consider alternatives to Cognos Analytics:

• Users or companies with smaller budgets or who want a straightforward, single pricing package; Cognos tends to have up-charges and add-ons that are only available at an additional cost.
• Users who require extensive customization capabilities, particularly for data visualizations, dashboards, and data exploration.
• Users who want a more advanced cloud deployment option.
• Users who have limited experience with BI and data analytics technology; this tool has a higher learning curve than many of its competitors and limited templates for getting started.
• Users who are already well established with another vendor ecosystem, like Microsoft or Google.

Who Shouldn’t Use Power BI

The following types of users and companies should consider alternatives to Power BI:

• Users who prefer to do their work online rather than on a mobile device; certain features are buggy outside of the mobile interface.
• Users who are not already well acquainted and integrated with the Microsoft ecosystem may face a steep learning curve.
• Users who prefer to manage their data in data warehouses rather than spreadsheets; while data warehouse and data lake integrations are available, including for Microsoft’s OneLake, many users run into issues with data quality in Excel.
• Users who prefer a more modern UI that updates in real time.
• Users who primarily use Macs and Apple products; some users have reported bugs when attempting to use Power BI Desktop on these devices.

Also see: Best Data Analytics Tools

If Cognos or Power BI Isn’t Ideal for You, Check Out These Alternatives

While Cognos and Power BI offer extensive features that will meet the needs of many BI teams and projects, they may not be the best fit for your particular use case. The following alternatives may prove a better fit:

Domo

Domo puts data to work for everyone so they can extend their data’s impact on the business. Underpinned by a secure data foundation, the platform’s cloud-native data experience makes data visible and actionable with user-friendly dashboards and apps. Domo is highly praised for its ability to help companies optimize critical business processes at scale and quickly.

Yellowfin

Yellowfin is a leading embedded analytics platform that offers intuitive self-service BI options. It is particularly successful at accelerating data discovery. Additionally, the platform allows anyone, from an experienced data analyst to a non-technical business user, to create reports in a governed way.

Wyn Enterprise

Wyn Enterprise offers a scalable embedded business intelligence platform without hidden costs. It provides BI reporting, interactive dashboards, alerts and notifications, localization, multitenancy, and white-labeling in a variety of internal and commercial apps. Built for self-service BI, Wyn offers extensive visual data exploration capabilities, creating a data-driven mindset for the everyday user. Wyn’s scalable, server-based licensing model allows room for your business to grow without user fees or limits on data size.

Zoho Analytics

Zoho Analytics is a top BI and data analytics platform that works particularly well for users who want self-service capabilities for data visualizations, reporting, and dashboarding. The platform is designed to work with a wide range of data formats and sources, and most significantly, it is well integrated with a Zoho software suite that includes tools for sales and marketing, HR, security and IT management, project management, and finance.

Sigma

Sigma is a cloud-native analytics platform that delivers real-time insights, interactive dashboards, and reports, so you can make data-driven decisions on the fly. With Sigma’s intuitive interface, you don’t need to be a data expert to dive into your data, as no coding or SQL is required to use this tool. Sigma has also recently brought forth Sigma AI features for early access preview.

Review Methodology

The two products in this comparison guide were assessed through a combination of reading product materials on vendor sites, watching demo videos and explanations, reviewing customer reviews across key metrics, and directly comparing each product’s core features through a comparison graph.

Below, you will see four key review categories that we focused on in our research. The percentages used for each of these categories represent the weight of the categorical score for each product.

User experience – 30%

Our review placed a heavy emphasis on user experience, considering both ease of use and implementation as well as the maturity and reliability of product features. We looked for features like AI assistance and low-code/no-code capabilities that lessened the learning curve, as well as learning materials, tutorials, and consistent customer support resources. Additionally, we paid attention to user reviews that commented on the product’s reliability and any issues with bugs, processing times, product crashes, or other performance issues.

Advanced analytics and scalability – 30%

To truly do business intelligence well, especially for modern data analytics requirements, BI tools need to offer advanced capabilities that scale well. For this review, we emphasized AI-driven insights, visuals that are configurable and updated in real time, shareable and collaborative reports and dashboards, and comprehensive features for data preparation, data modeling, and data explainability. As far as scalability goes, we not only looked at the quality of each of these tools but also assessed how well they perform and process data on larger-scale operations. We particularly highlighted any user reviews that mentioned performance lag times or other issues when processing large amounts of data.

Integrations and platform flexibility – 20%

Because these platforms need to be well integrated into a business’s data sources and most-used business applications to be useful, our assessment also paid attention to how integrable and flexible each platform was for different use cases. We considered not only how each tool integrates with other tools from the same vendor but also which data sources, collaboration and communication applications, and other third-party tools are easy to integrate with native integrations and connectors. We also considered the quality of each tool’s APIs and other custom opportunities for integration, configuration, and extensibility.

Affordability – 20%

While affordability is not the be-all-end-all when it comes to BI tools, it’s important to many users that they find a tool that balances an accessible price point with a robust feature set. That’s why we also looked at each tool’s affordability, focusing on entry price points, what key features are and are not included in lower-tier pricing packages, and the jumps in pricing that occur as you switch from tier to tier. We also considered the cost of any additional add-ons that users might need, as well as the potential cost of partnering with a third-party expert to implement the software successfully.

Bottom Line: Cognos vs. Power BI

Microsoft is committed to investing heavily in Power BI and enhancing its integrations across other Microsoft platforms and a growing number of third-party solutions. Any organization that is a heavy user of Office 365, Teams, Dynamics, and/or Azure will find it hard to resist the advantages of deploying Power BI.

And those advantages are only going to increase. On the AI front, for example, the company boasts around 100,000 customers using Power BI’s AI services. It is also putting effort into expanding its AI capabilities, with the generative AI-driven Copilot now in preview for Power BI users. For users with an eye on their budget who don’t want to compromise on advanced analytics and BI features, Power BI is an excellent option.

But IBM isn’t called Big Blue for nothing. It boasts a massive sales and services team and global reach into large enterprise markets. It has also vastly expanded its platform’s AI capabilities, making it a strong tool for democratized data analytics and advanced analytics tasks across the board.

Where Cognos Analytics has its most distinct advantage is at the high end of the market. Microsoft offers most of the features that small, midsize, and larger enterprises need for analytics. However, at the very high end of the analytics market, and in corporate environments with hefty governance and reporting requirements or legacy and on-premises tooling, Cognos has carved out a strategic niche that it serves well.

Ultimately, either tool could work for your organization, depending on your budget, requirements, and previous BI tooling experience. The most important step you can take is to speak directly with representatives from each of these vendors, demo these tools, and determine which product includes the most advantageous capabilities for your team.

Read next: 10 Best Machine Learning Platforms

The post Cognos vs. Power BI: 2024 Data Platform Comparison appeared first on eWEEK.

As two well-regarded data analytics platforms in the BI space, users may have a difficult time deciding between Looker and Power BI for their data management requirements. There are arguments for and against each, and in this comparison guide, we’ll dive deeper into core features, pros, cons, and pricing for Looker and Power BI.

But before we go any further, here’s a quick summary of how each product stands out against its competitors:

• Looker: Best for current Google product users and others who are most interested in highly configurable and advanced analytics capabilities, including data visualizations and reporting. Looker Studio in particular balances ease of use with high levels of customization and creativity, while also offering users a lower-cost version of an otherwise expensive platform.
• Power BI: Best for current Microsoft product users and others who want an easy-to-use and affordable BI tool that works across a variety of data types and use cases. This is considered one of the most popular BI tools on the market and meets the needs of a variety of teams, budgets, and experience levels, though certain customizations and big data processing capabilities are limited.

Looker vs. Power BI at a Glance

What Is Looker?

Looker is an advanced business intelligence and data management platform that can be used to analyze and build data-driven applications, embed data analytics in key organizational tools, and democratize data analysis in a way that preserves self-service capabilities and configurability. The platform has been managed by Google since its acquisition in 2019, and because of its deep integration within the Google ecosystem, it is a favorite among Google Cloud and Workspace users for unified analytics projects. However, the tool also works well with other cloud environments and third-party applications, as it maintains a fairly intuitive and robust collection of integrations.

Key features of Looker

• Comprehensive data visualization library: In addition to giving users the ability to custom-configure their visualizations to virtually any parameters and scenarios, Looker’s data visualization library includes a wide range of prebuilt visual options. Traditional visuals like bar graphs and pie charts are easy to access, and more complex visuals — like heatmaps, funnels, and timelines — can also be easily accessed.
• “Blocks” code snippets: Instead of reinventing the wheel for certain code snippets and built-out data models, Looker Blocks offers prebuilt data models and code to help users quickly develop high-quality data models. Industry-specific, cloud-specific, and data-source-specific blocks are all available, which makes this a great solution for users of all backgrounds who want to get started with complex models more quickly.
• Governed and integrated data modeling: With its proprietary modeling language and emphasis on Git-driven data storage and rule development, users can easily build trusted and governed data sources that make for higher-quality and more accurate data models, regardless of how many teams are working off of these models.

Pros

• Looker comes with a large library of prebuilt integrations — including for many popular data tools — and also offers user-friendly APIs for any additional integrations your organization may need to set up.
• Looker’s visualizations and reports are easy to customize to your organization’s more specific project requirements and use cases; it also offers one of the more diverse visualization libraries in this market.
• LookML allows users to create centralized governance rules and handle version control tasks, ensuring more accurate outcomes and higher quality data, even as data quantities scale.

Cons

• On-premises Looker applications do not easily connect to Looker Studio and other cloud-based tools in user portfolios, which severely limits the ability to maintain data projects accurately and in real time for on-prem users.
• Looker uses its own modeling language, which can make it difficult for new users to get up and running quickly.
• Some users have had trouble with self-service research and the vendor’s documentation.

What Is Power BI?

Microsoft Power BI is a business intelligence and data visualization solution that is one of the most popular data analytics tools on the market today. As part of the Microsoft Power Platform, the tool is frequently partnered with Microsoft products like Power Automate, Power Apps, and Power Pages to get the most out of data in different formats and from different sources. Its focus on ease of use makes it a leading option for teams of all backgrounds; especially with the growth of its AI-powered assistive features, visualization templates, and smooth integrations with other Microsoft products, it has become one of the best solutions for democratized data science and analytics.

Key features of Power BI

• AI-driven analytics: AI-powered data analysis and report creation have already been established in this platform, but recently, the generative AI Copilot tool has also come into preview for Power BI. This expands the platform’s ability to create reports more quickly, summarize and explain data in real time, and generate DAX calculations.
• Dynamics 365 integration: Power BI integrates relatively well with the Microsoft Dynamics CRM, which makes it a great option for in-depth marketing and sales analytics tasks. Many similar data platforms do not offer such smooth CRM integration capabilities.
• Comprehensive mobile version: Unlike many other competitors in this space, Microsoft Power BI comes with a full-featured, designed-for-mobile mobile application that is available at all price points and user experience levels. With native mobile apps available for Windows, iOS, and Android, any smartphone user can quickly review Power BI visualizations and dashboards from their personal devices.

Pros

• Power BI can be used in the cloud, on-premises, and even as an embedded solution in other applications.
• The user interface will be very familiar to users who are experienced with Microsoft products; for others, the platform is accompanied by helpful training resources and ample customer support.
• This platform makes democratized data analytics simpler, particularly with AI-powered features and a growing generative AI feature set.

Cons

• While some users appreciate that Power BI resembles other Microsoft 365 office suite interfaces, other users have commented on the outdated interface and how it could be improved to look more like other cloud-based competitors.
• Especially with larger quantities of data, the platform occasionally struggles to process data quickly and accurately; slower load times, crashes, and bugs are occasionally introduced during this process.
• Visualizations are not very customizable, especially compared to similar competitors.

Best for Core Features: It Depends

Both Looker and Power BI offer all of the core features you would expect from a data platform, including data visualizations, reporting and dashboarding tools, collaboration capabilities, and integrations. They also offer additional features to assist users with their analytical needs. Power BI offers support through AI assistance and Looker supports users with prebuilt code snippets and a diverse integration and plugin marketplace.

Microsoft maintains a strong user base with its full suite of data management features and easy-to-setup integrations with other Microsoft tools. It can be deployed on the cloud, on-premises, and in an embedded format, and users can also access the tool via a comprehensive mobile application.

Looker is web-based and offers plenty of analytics capabilities that businesses can use to explore, discover, visualize, and share analyses and insights. Enterprises can use it for a wide variety of complex data mining techniques. It takes advantage of a specific modeling language to define data relationships while bypassing SQL. Looker is also tightly integrated with a great number of Google datasets and tools, including Google Analytics, as well as with several third-party data and business tools.

Looker earns good marks for reporting granularity, scheduling, and extensive integration options that create an open and governable ecosystem. Power BI tends to perform better than Looker in terms of breadth of service due to its ecosystem of Microsoft Power Platform tools; users also tend to prefer Power BI for a comprehensive suite of data tools that aren’t too difficult to learn how to use.

Because each tool represents such a different set of strengths, it’s a tie for this category.

Best for Ease of Use and Implementation: Power BI

In general, users who have tried out both tools find that Power BI is easier to use and set up than Looker.

Power BI provides users with a low-code/no-code interface as well as a drag-and-drop approach to its dashboards and reports. Additionally, its built-in AI assistance — which continues to expand with the rise of Copilot in Power BI — helps users initiate complex data analytics tasks regardless of their experience with this type of technology or analysis.

For some users, Looker has a steep learning curve because they must learn and use the LookML proprietary programming language to set up and manage their models in the system. This can be difficult for users with little experience with modeling languages, but many users note that the language is easy to use once they’ve learned its basics. They add that it streamlines the distribution of insights to staff across many business units, which makes it a particularly advantageous approach to data modeling if you’re willing to overcome the initial learning curve.

The conclusion: Power BI wins on general use cases for a non-technical audience whereas Looker wins with technical users who know its language.

Best for Advanced Data Analytics: Looker

While both tools offer unique differentiators for data analytics operations, Looker outperforms Power BI with more advanced, enterprise-level data governance, modeling, and analytics solutions that are well integrated with common data sources and tools.

Both tools offer extensive visualization options, but Looker’s data visualizations and reporting are more customizable and easier to configure to your organization’s specs and stakeholders’ expectations. Looker also streamlines integrations with third-party data tools like Slack, Segment, Redshift, Tableau, ThoughtSpot, and Snowflake, while also working well with Google data sources like Google Analytics. As far as its more advanced data analytics capabilities go, Looker surpasses Power BI and many other competitors with features like granular version control capabilities for reports, comprehensive sentiment analysis and text mining, and open and governed data modeling strategies.

However, Looker has limited support for certain types of analytics tasks, like cluster analysis, whereas Power BI is considered a top tool in this area. And, so far, Power BI does AI-supported analytics better, though Google does not appear to be too far behind on this front.

It’s a pretty close call, but because of its range of data analytics operations and the number of ways in which Google makes data analytics tasks customizable for its users, Looker wins in this category.

Also see: Best Data Analytics Tools 

Best for Integrations: It Depends

When it comes to integrations, either Power BI or Looker could claim the upper hand here.

It all depends on if you’re operating in a Microsoft shop or a Google shop. Current Microsoft users will likely prefer Power BI because of how well it integrates with Azure, Dynamics 365, Microsoft 365, and other Microsoft products. Similarly, users of Google Cloud Platform, Google Workspace, and other Google products are more likely to enjoy the integrated experience that Looker provides with these tools.

If your organization is not currently working with apps from either of these vendor ecosystems, it may be difficult to set up certain third-party integrations with Power BI or Looker. For example, connecting Power BI to a collaboration and communication tool like Slack generally requires users to use Microsoft Power Automate or an additional third-party integration tool. Looker’s native third-party integrations are also somewhat limited, though the platform does offer easy-to-setup integrations and actions for tools like Slack and Segment.

Because the quality of each tool’s integrations depends heavily on the other tools you’re already using, Power BI and Looker tie in this category.

Best for Pricing: Power BI

Power BI is consistently one of the most affordable BI solutions on the market. And while Looker Studio in particular helps to lower Looker’s costs, the platform is generally considered more expensive.

Power BI can be accessed through two main free versions: Power BI Desktop and a free account in Microsoft Fabric. The mobile app is also free and easy to access. But even for teams that require more functionality for their users, paid plans are not all that expensive. Power BI Pro costs only $10 per user per month, while Power BI Premium is $20 per user per month.

Looker, on the other hand, is more expensive, requiring users to pay a higher price for its enterprise-class features. The Standard edition’s pay-as-you-go plan costs $5,000 per month, while all other plans require an annual commitment and a conversation with sales to determine how much higher the costs will be.

Additionally, there are user licensing fees that start at $30 per month for a Viewer User; users are only able to make considerable changes in the platform as either a Standard User or a Developer User, which costs $60 and $125 per user per month respectively.

Power BI takes the lead when it comes to pricing and general affordability across its pricing packages.

Also see: Top Digital Transformation Companies

Why Shouldn’t You Use Looker or Power BI?

While Looker and Power BI are both favorites among data teams and citizen data scientists alike, each platform has unique strengths — and weaknesses — that may matter to your team. If any of the following qualities align with your organizational makeup, you may want to consider investing in a different data platform.

Who Shouldn’t Use Looker

The following types of users and companies should consider alternatives to Looker:

• Users who want an on-premises BI tool; most Looker features, including useful connections to Looker Studio, are only available to cloud users.
• Users who are not already working with other Google tools and applications may struggle to integrate Looker with their most-used applications.
• Users with limited computer-language-learning experience may struggle, as most operations are handled in Looker Modeling Language (LookML).
• Users who want a lower-cost BI tool that still offers extensive capabilities to multiple users.
• Users in small business settings may not receive all of the vendor support and affordable features they need to run this tool successfully; it is primarily designed for midsize and larger enterprises.

Who Shouldn’t Use Power BI

The following types of users and companies should consider alternatives to Power BI:

• Users who need more unique and configurable visualizations to represent their organization’s unique data scenarios.
• Users who are not already working with other Microsoft tools and applications may struggle to integrate Power BI into their existing tool stack.
• Users who consistently process and work with massive quantities of data; some user reviews indicate that the system gets buggy and slow with higher data amounts.
• Users who work with a large number of third-party data and business apps; Power BI works best with other Microsoft tools, especially those in the Power Platform.
• Users who consistently need to run more complex analytics, such as predictive analytics, may need to supplement Power BI with other tools to get the results they need.

If Looker or Power BI Isn’t Ideal for You, Check Out These Alternatives

Both Looker and Power BI offer extensive data platform features and capabilities, as well as smooth integrations with many users’ most important data sources and business applications. However, these tools may not be ideally suited to your team’s particular budget, skill sets, or requirements. If that’s the case, consider investing in one of these alternative data platform solutions:

Domo

Domo puts data to work for everyone so they can extend their data’s impact on the business. Underpinned by a secure data foundation, the platform’s cloud-native data experience makes data visible and actionable with user-friendly dashboards and apps. Domo is highly praised for its ability to help companies optimize critical business processes at scale and quickly.

Yellowfin

Yellowfin is a leading embedded analytics platform that offers intuitive self-service BI options. It is particularly successful at accelerating data discovery. Additionally, the platform allows anyone, from an experienced data analyst to a non-technical business user, to create reports in a governed way.

Wyn Enterprise

Wyn Enterprise offers a scalable embedded business intelligence platform without hidden costs. It provides BI reporting, interactive dashboards, alerts and notifications, localization, multitenancy, and white-labeling in a variety of internal and commercial apps. Built for self-service BI, Wyn offers extensive visual data exploration capabilities, creating a data-driven mindset for the everyday user. Wyn’s scalable, server-based licensing model allows room for your business to grow without user fees or limits on data size.

Zoho Analytics

Zoho Analytics is a top BI and data analytics platform that works particularly well for users who want self-service capabilities for data visualizations, reporting, and dashboarding. The platform is designed to work with a wide range of data formats and sources, and most significantly, it is well integrated with a Zoho software suite that includes tools for sales and marketing, HR, security and IT management, project management, and finance.

Sigma

Sigma is a cloud-native analytics platform that delivers real-time insights, interactive dashboards, and reports, so you can make data-driven decisions on the fly. With Sigma’s intuitive interface, you don’t need to be a data expert to dive into your data, as no coding or SQL is required to use this tool. Sigma has also recently brought forth Sigma AI features for early access preview.

Review Methodology

Looker and Power BI were reviewed based on a few core standards and categories for which data platforms are expected to perform. The four categories covered below have been weighted according to how important they are to user retention over time.

User experience – 30%

When it comes to user experience, we paid attention to how easy each tool is to use and implement and how many built-in support resources are available for users who have trouble getting started. Additionally, we considered how well the platform performs under certain pressures, like larger data loads, security and user control requirements, and more complex modeling and visualization scenarios. Finally, we considered the availability of the tool in different formats and how well the tool integrates with core business and data applications.

Scalability and advanced analytics compatibility – 30%

Our review also considered how well each platform scales to meet the needs of more sophisticated analytics operations and larger data processing projects. We paid close attention to how the platform performs as data loads grow in size and complexity, looking at whether user reviews mention any issues with lag times, bugs, or system crashes. We also considered what tools were available to assist with more complex analytics tasks, including AI-powered insights and support, advanced integrations and plugins, and customizable dashboards and reports.

Integrability – 20%

We considered how well each tool integrated with other software and cloud solutions from the same vendor as well as how easy it is to set up third-party integrations either via prebuilt connectors or capable APIs. In particular, we examined how well each platform integrated with common data sources outside of its vendor ecosystem, including platforms like Redshift, Snowflake, Salesforce, and Dropbox.

Cost and accessibility – 20%

For cost and accessibility, we not only focused on low-cost solutions but also on how well each solution’s entry-level solutions perform and meet user needs. We assessed the user features available at each pricing tier, how quickly pricing rises — especially for individual user licenses or any required add-ons, and whether or not a comprehensive free version was available to help users get started.

Bottom Line: Looker vs. Power BI

Microsoft’s Power BI has consistently been among the top two and three business intelligence tools on the market, recruiting and retaining new users with its balance of easy-to-use features, low costs, useful dashboards and visualizations, range of data preparation and management tools, AI assistance, and Microsoft-specific integrations. It is both a great starter and advanced data platform solution, as it offers the features necessary for citizen data scientists and more experienced data analysts to get the most out of their datasets.

Power BI tends to be the preferred tool of the two because of its general accessibility and approachability as a tool, but there are certain enterprise user needs for reporting and analytics distribution where Looker far outperforms Power BI. And for those heavily leaning on Google platforms or third-party applications, Looker offers distinct advantages to skilled analysts.

Ultimately, Looker doesn’t really try to compete head-to-head with Microsoft, because they each target different data niches and scenarios. It’s often the case that prospective buyers will quickly be able to identify which of these tools is the best fit for their needs, but if you’re still not sure, consider reaching out to both vendors to schedule a hands-on demo.

Read next: Best Data Mining Tools and Software

The post Looker vs. Power BI: 2024 Software Comparison appeared first on eWEEK.

Generally considered a subset of artificial intelligence (AI), machine learning systems generate algorithms based on training datasets and then deliver relevant outputs, often without expressly being programmed to produce the exact outcomes they drive.

The autonomous learning capabilities of AI and ML platforms are at the center of today’s enterprises. The technology is increasingly being used to make important decisions and drive automations that improve enterprise operations across disciplines. In recent years, ML technology has also formed the foundation for generative AI models, which are trained to generate new content through larger datasets and more complex ML algorithms.

With its range of relevant business use cases in the modern enterprise, machine learning platform technology has quickly grown in popularity, and vendors have expanded these platforms, capabilities and offerings to meet growing demands.

In this guide, we cover 10 of the best machine learning platforms on the market today, detailing their specific features, pros and cons, and any areas where they particularly stand out from the competition.

Best Machine Learning Software: Comparison Chart

Top 10 Machine Learning Software Platforms

Alteryx Machine Learning: Best for Citizen Data Scientists and Developers

Alteryx has emerged as a leader in the machine learning space for tackling extremely complex machine learning projects through an accessible interface. The drag-and-drop platform incorporates highly automated ML features for both experienced data scientists and less technical business users. Many users particularly praise this platform for its built-in Education Mode, which makes the no-code platform even easier to learn and adjust to your particular use cases.

The platform connects to an array of open-source GitHub libraries — including Woodwork, Compose, Featuretools, and EvalML — and handles numerous data formats and sources. Alteryx also offers powerful visualization tools and feature engineering tools as well as a large and active user community.

Pricing

Pricing information for Alteryx Machine Learning is only available upon request. Prospective buyers can contact Alteryx directly for more information and/or get started with the product’s free trial on either desktop or cloud.

Key Features

• Automated machine learning and feature engineering.
• Automated insight generation for data relationships.
• Built-in Education Mode for learning and optimizing ML development.
• Access to open-source packages and libraries in GitHub.
• No-code, cloud-based format.

Pros

• Offers strong data prep and integration tools along with a robust set of curated algorithms.
• Excellent interface and powerful automation features.

Cons

• Macros and APIs for connecting to various data sources can be difficult to set up and use.
• Some users complain about slow load and processing speeds.

Databricks Data Intelligence Platform: Best for Enterprise-Scale Data Management and Feature Engineering

The Databricks Data Intelligence Platform offers a centralized environment with powerful tools and features that facilitate machine learning and the data preparation work that goes into successful ML model developments.

Managed MLflow is one standout feature that relies on an open-source platform developed by Databricks to manage complex interactions across the ML lifecycle. This platform is particularly useful for organizations that want a combination of self-service and guided data management and feature engineering capabilities that work for data from disparate sources and in different formats.

Interested users can take advantage of the platform for data processing and preparation — including for generative AI and large language models — and to prepare data production pipelines. They can also register and manage models through the Model Registry feature. In addition, the platform provides users with collaborative notebooks, the Feature Registry, and the Feature Provider, all of which support feature engineering requirements and MLOps with a strong, big-data-driven backbone.

Pricing

The Databricks platform is available at no base cost; instead, interested users will sign up and then pay for the features and quantities they use on a per-second basis. Users with larger usage requirements may be eligible for committed use discounts, which work across cloud environments. If you have inconsistent or smaller usage requirements, you’ll need to pay per product and per Databricks Unit (DBU) used:

• Workflows & Streaming Jobs: Starting at $0.07 per DBU.
• Workflows & Streaming Delta Live Tables: Starting at $0.20 per DBU.
• Data Warehousing Databricks SQL: Starting at $0.22 per DBU.
• Data Science & Machine Learning All Purpose Compute for Interactive Workloads: Starting at $0.40 per DBU.
• Data Science & Machine Learning Serverless Real-Time Inference: Starting at $0.07 per DBU.
• Databricks Platform & Add-Ons: Information available upon request.

A 14-day free trial is also available with limited features.

Key Features

• Open lakehouse architecture.
• REST-API-driven model deployment.
• Pretrained and fine-tuned LLM integration options.
• Self-service data pipelines.
• Managed MLflow with experiment tracking and versioning.

Pros

• The open data lakehouse format makes it easier to work with data from different sources and for different use cases; users appreciate that the platform can scale for data orchestration, data warehousing, advanced analytics, and data preparation for ML, even for larger datasets.
• This is a highly scalable environment with excellent performance in a framework that users generally find easy to use; many features are built on open-source data technologies.

Cons

• Can be pricey, especially when compared to completely free and open-source solutions in this space.
• Some visualization features are limited and difficult to set up.

Dataiku: Best for Extensibility

Dataiku is a popular, user-friendly ML platform that delivers all the tools required to build robust ML models, including strong data preparation features. An AutoML feature is another great component of the tool that is designed to fill in missing values and seamlessly convert non-numerical data into numerical values. Its data preparation, visualization, and feature engineering capabilities are well-reviewed components of the platform, but where Dataiku really sets itself apart is its extensibility and range of integrations.

Users can easily integrate many of today’s top generative AI services and platforms, including from OpenAI, Cohere, Anthropic, and Hugging Face. A range of public and proprietary plugins are available through GUI-based code packages, and integrations are also available with leading DevOps and data science visualization frameworks. Dataiku also supports custom modeling using Python, R, Scala, Julia, Pyspark, and other languages.

Pricing

Four plan options are available for Dataiku users. Pricing information is not provided for the paid plans, though other details about what each plan covers are included on the pricing page. A 14-day free trial is also available for each of the paid plans listed below:

• Free Edition: $0 for up to three users and installation on your personal infrastructure. Other limited features are included.
• Discover: A paid plan for up to five users that includes more than 20 database connectors, Spark-based data processing, and limited automations. Pricing information is available upon request.
• Business: A paid plan for up to 20 users that includes unlimited Kubernetes-based computations, full automation, and advanced security features. Pricing information is available upon request.
• Enterprise: A paid plan that includes all database connectors, full deployment capabilities, an isolation framework, and unlimited instances and resource governance. Pricing information is available upon request.

Key Features

• Feature store and automatic feature generation.
• Generative AI platform integrations.
• White-box explainability for ML model development.
• Prompt Studios for prompt-based LLM model development.
• Public and proprietary plugins for custom visual recipes, connectors, processors, and more.

Pros

• Dataiku is among the most flexible machine learning platforms, and it delivers strong training features.
• Dataiku easily integrates and extends its functionalities with third-party DevOps, data science visualization, and generative AI tools, frameworks, and services.

Cons

• Dataiku has a somewhat unconventional development process that can slow down model development.
• Especially as the tool updates, some users have experienced difficulties with outages.

Also see: Best Data Analytics Tools

Vertex AI: Best for Model Organization and Management

The Vertex AI platform is a leading cloud-based AI and ML solution that taps into the power of Google Cloud to deliver a complete set of tools and technologies for building, deploying, and scaling ML models. It supports pre-trained custom tooling, AutoML APIs that speed up model development, and a low-code framework that typically results in 80% fewer lines of code.

It’s also a highly organized platform that gives users accessible tools to manage their models at all stages of development. For example, the Vertex AI Model Registry is available for users who want a central repository where they can import their own models, create new models, classify models as ready for production, deploy models to an endpoint, evaluate models, and look at ML models both at a granular level and in an overview format. Additionally, Vertex AI supports nearly all open-source frameworks, including TensorFlow, PyTorch, and scikit-learn.

Pricing

Pricing for Vertex AI is highly modular and based on the tools and services, compute, and storage you use, as well as any other Google Cloud resources you use for ML projects. We’ll cover the estimates for some of the most commonly used features below, but it’s a good idea to use the pricing calculator or contact Google directly for a custom quote that fits your particular needs:

• Generative AI (Imagen model for image generation): Starting at $0.0001.
• Generative AI (Text, chat, and code generation): Starting at $0.0001 per 1,000 characters.
• AutoML Models (Image data training, deployment, and prediction): Starting at $1.375 per node hour.
• AutoML Models (Video data training and prediction): Starting at $0.462 per node hour.
• AutoML Models (Text data upload, training, deployment, prediction): Starting at $0.05 per hour.
• Vertex AI Pipelines: Starting at $0.03 per pipeline run.

A free trial is available for Vertex AI as well, though only as part of a greater free trial for all of Google Cloud. The Google Cloud free trial gives all users $300 in free credits to test out the platform.

Key Features

• Model Garden library with models that can be customized and fine-tuned.
• Native MLOps tools, including Vertex AI Evaluation, Vertex AI Pipelines, and Feature Store.
• Custom ML model training workflows.
• Vertex AI prediction service with custom prediction routines and prebuilt containers.
• Vertex AI Model Registry for production-ready model deployment.

Pros

• Despite powerful ML capabilities, the platform is fairly user-friendly, relatively easy to use, and highly scalable.
• It delivers strong integrations with other Google solutions, including BigQuery and Dataflow.

Cons

• Vertex AI is not as flexible and as customizable as other ML platforms. It also lacks support for custom algorithms.
• Some users complain about the high price and limited support for languages beyond Python.

H2O-3: Best for R and Python Programmers

H2O-3 is the latest iteration of the open-source data science platform that supports numerous areas of AI, including machine learning. The platform is designed with numerous automation features, including feature selection, feature engineering, hyperparameter autotuning, model ensembling, label assignment, model documentation, and machine learning interpretability (MLI).

H2O-3 offers powerful features specifically designed for Natural Language Processing (NLP) and computer vision. R and Python programmers particularly appreciate this platform for its wide-ranging community support and easy download options that are compatible with the two languages.

Pricing

H2O-3 is a free and open-source solution that users can download directly from the vendor site or in AWS, Microsoft Azure, or Google Cloud.

Key Features

• Open-source, distributed, in-memory format.
• Support for gradient-boosted machines, generalized linear models, and deep learning models.
• AutoML-driven leaderboard for model algorithms and hyperparameters.
• Algorithms include Random Forest, GLM, GBM, XGBoost, GLRM, and Word2Vec.
• H2O Flow for no-code interface option; code-based options include R and Python.

Pros

• Excellent support for open-source tools, components, and technologies.
• Offers powerful bias detection and model scoring features.

Cons

• Some users complain about missing analysis tools and limited algorithm support.
• Overall performance and customer support lag behind competitors.

KNIME Analytics Platform: Best for Community-Driven ML Development

The KNIME Analytics Platform promotes an end-to-end data science framework designed for both technical and business users. This includes a comprehensive set of automation tools for tackling machine learning and deep learning. The KNIME platform delivers a low-code/no-code visual programming framework for building and managing models.

The platform includes a robust set of data integration tools, filters, and reusable components that can be shared within a highly collaborative framework. Speaking of collaboration, the KNIME community is one of the most active and collaborative open-source communities in this space. Users can additionally benefit from KNIME Community Hub, a separate software solution that allows users to collaborate with data science and business users from other organizations and review other users’ samples with few overhead limitations.

Pricing

KNIME is a free and open-source solution, though interested users may want to contact the vendor directly to determine if their particular use case will incur additional costs. The KNIME Analytics Platform can be freely downloaded on Windows, Mac, and Linux.

Key Features

• Open-source, low-code/no-code tooling.
• Drag-and-drop analytic workflows.
• Access to ML libraries like TensorFlow, Keras, and H2O.
• Workflow-building node repository and workflow editor.
• AutoML for automated binary and multiclass classification and supervised ML training.

Pros

• Provides an intuitive, low-code/no-code interface that makes it easy for non-data scientists and new users to build ML models.
• Delivers strong automation capabilities across the spectrum of ML tasks.

Cons

• Code-based scripting requirements through Python and R can introduce challenges for certain types of customizations.
• Some users complain that the platform is prone to consume excessive computational resources.

Also see: Top Data Mining Tools

MATLAB: Best for Supportive ML Apps and Trainings

MathWorks MATLAB is popular among engineers, data scientists, and others looking to construct sophisticated machine learning models. It includes point-and-click apps for training and comparing models, advanced signal processing and feature extraction techniques, and AutoML, which supports feature selection, model selection, and hyperparameter tuning.

MATLAB works with popular classification, regression, and clustering algorithms for supervised and unsupervised learning. And, despite its many complex features and capabilities, it is a relatively accessible tool that offers a range of detailed training and documentation to users, as well as accessible and easy-to-incorporate apps.

Pricing

MATLAB can be used by organizations and individuals of all different backgrounds and is sometimes used in combination with Simulink, a MATLAB-based environment for multidomain model programming. Multiple subscription options are available:

• Standard: $940 per year, or $2,350 for a perpetual license.
• MATLAB and Simulink Startup Suite: $3,800 per year.
• Academic: $275 per year, or $550 for a perpetual license.
• MATLAB and Simulink Student Suite: $99 for a perpetual license.
• Home/personal use: $149 for a perpetual license.

A 30-day free trial option is available for MATLAB, Simulink, and several other products.

Key Features

• Prebuilt MATLAB apps and toolboxes.
• Live Editor for scripting.
• Simulink for model-based design.
• Classification Learner App for data classification and training.
• Onramp, interactive examples, tutorials, and e-books for getting started with machine learning.

Pros

• The platform offers an array of powerful tools and capabilities within a straightforward user interface that is particularly friendly to advanced mathematical, research, and data science use cases.
• Extremely flexible, with excellent collaboration features, app integration opportunities, and scalability.

Cons

• Relies on a somewhat proprietary approach to machine learning. Lacks support for some open-source components and languages, which can also make the tool more expensive than other players in this space.
• Can be difficult to use for business constituents and other non-data scientists to get started, though the platform comes with extensive training options to bridge that gap.

Azure Machine Learning: Best for LLM Development

Automation is at the center of Azure Machine Learning. The low-code platform boasts 70% fewer steps for model training and 90% fewer lines of code for pipelines. It also includes powerful data preparation tools and data labeling capabilities, along with collaborative notebooks, which makes it a great one-stop shop for MLOps requirements.

As modern use cases for machine learning have drifted more and more toward generative AI, Azure Machine Learning has proven itself a leader in this type of ML model development. Users can track and optimize training prompts with prompt flow, improve outcomes with the Responsible AI dashboard, benefit from scalable GPU infrastructure, and work within a wide range of tools and frameworks.

Pricing

Similar to many other platforms in this space, Azure Machine Learning itself comes at no cost, but users will quickly rack up costs based on the compute and other Azure services they use. Pricing is highly variable for this tool, so we’ve only included estimates and starting prices for a few key compute options; prospective buyers should contact Microsoft directly for additional pricing information beyond what we’ve included here:

• D2-64 v3: Individual components range from $0 per hour to $2.67 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.  For this option and the ones below, many of these costs will be stacked on top of each other, depending on which instance you select.
• D2s-64s v3: Individual components range from $0 per hour to $3.072 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.
• E2-64 v3: Individual components range from $0 per hour to $1.008 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.
• M-series: Individual components range from $0 per hour to $26.688 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.
• H-series: Individual components range from $0 per hour to $2.664 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.

Discounted prices may be available for stable and predictable workloads through Azure Reserved Virtual Machine Instances. A free trial of Azure is also available.

Key Features

• Open-source library and framework interoperability.
• Responsible AI framework and dashboard.
• Prompt flow for AI workflow orchestration, including for LLMs.
• Data preparation and labeling.
• Drag-and-drop designer with notebooks, automated machine learning, and experiments.
• Managed endpoints for model deployment and scoring.

Pros

• The drag-and-drop interface and low-code framework simplify ML model building.
• Extensive LLM development and optimization features are available; the platform also benefits from Microsoft’s deep investment in generative AI and OpenAI in particular.

Cons

• The pricing structure is difficult to understand and can quickly get expensive
• Some users complain about subpar documentation and difficulties with support.

RapidMiner: Best for Cross-Disciplinary Teams

RapidMiner is an ML platform vendor that promotes the idea of “intuitive machine learning for all” through both code-based ML and visual low-code tools that non-technical team members can learn how to use. The platform includes prebuilt templates for common use cases, as well as guided modeling capabilities. It also provides robust tools for validating and retesting models.

RapidMiner focuses on MLOps and automated data science through several key functions, including an auto engineering feature and automatic process explanations. It is a highly collaborative platform with a project-based framework, co-editing capabilities, and built-in user authentication and access control features.

Pricing

A free version of RapidMiner, called RapidMiner Studio Free, is available for desktop users who require no more than 10,000 data rows and one logical processor. The enterprise version of the platform is a paid subscription; prospective buyers will need to contact RapidMiner directly for specific pricing information. All users can benefit from a 30-day free trial of the full platform, and discounts are available for certain groups, including academics.

Key Features

• Codeless model ops.
• Accurate and finance-based model scoring.
• Built-in drift prevention.
• Native dashboards and reports and integrations with BI platforms.
• User-level choice between code-based, visual, and automated model creation with logging for all options.

Pros

• A strong focus on administrative controls for governance, reporting, and user access.
• Offers intuitive, low-code/no-code tools for non-data scientists as well as sophisticated code-based tools for data scientists.

Cons

• Some users complain about the heavy computational resource requirements involved with using RapidMiner.
• Can be crash-prone in certain situations and scenarios.

TensorFlow: Best for MLOps

TensorFlow is an open-source machine learning software library that extends itself beyond this primary role to support end-to-end machine learning platform requirements. It works well for basic ML model development but also has the resources and capacity to support more complex model developments, including for neural networks and deep learning models.

Although TensorFlow rarely labels itself as an MLOps platform, it offers all of the open-source flexibility, extensibility, and full-lifecycle capabilities MLOps teams need to prepare their data, build models, and deploy and monitor models on an ongoing basis. TensorFlow Extended (TFX) is a particularly effective version of the tool for creating scalable ML pipelines, training and analyzing models, and deploying models in a production-ready environment.

Pricing

TensorFlow is a free and open-source tool, though additional costs may be incurred, depending on other tools you choose to integrate with the platform. The tool can be deployed directly on the web, on servers, or on mobile or edge devices.

Key Features

• Pretrained models in the model garden and TensorFlow Hub.
• On-premises, mobile-device, browser, and cloud-based deployment options.
• Simple ML add-on for Google Sheets model training and evaluation.
• Production-ready ML pipelines.
• Data preparation and responsible AI tools to eliminate data bias.

Pros

• Many other platforms, including those on this list, are compatible with TensorFlow and its software library.
• TensorFlow is known for its helpful and active user community.

Cons

• The models you can build within TensorFlow are mostly static, which may not be the most agile option.
• Many users have commented on how it’s more difficult to use and understand than most other Python-based software libraries.

Also see: Real-Time Data Management Trends

Key Features of Machine Learning Software

While the goal is typically the same — solving difficult computing problems — machine learning software varies greatly. It’s important to review vendors and platforms thoroughly and understand how different features and tools work. The following key features are some of the most important to consider when selecting machine learning software:

Data Processing and Ingestion

It’s important to understand how the software ingests data, what data formats it supports, and whether it can handle tasks such as data partitioning in an automated way. Some packages offer a wealth of templates and connectors, while others do not.

Support for Feature Engineering

Feature engineering is crucial for manipulating data and building viable algorithms. The embedded intelligence converts and transforms strings of text, dates, and other variables into meaningful patterns and information that the ML system uses to deliver results.

Algorithm and Framework Support

Modern ML platforms typically support multiple algorithms and frameworks; this flexibility is crucial. In some cases, dozens or hundreds of algorithms may be required for a business process. Yet, it’s also important to have automated algorithm selection capabilities that suggest and match algorithms with tasks. This feature typically reduces complexity and improves ML performance. Additionally, having access to a range of framework options gives users more agility when automating ML development tasks.

Training and Tuning Tools

It’s vital to determine how well algorithms function and what business value the ML framework delivers. Most users benefit from smart hyperparameter tuning, which simplifies the ability to optimize each algorithm. Various packages include different tools and capabilities, and, not surprisingly, some work better for certain types of tasks and algorithms. Especially with large language models and other larger ML models, you’ll want to identify tools that make training and fine-tuning easy, regardless of your particular use cases.

Ensembling Tools

Within ML, it’s common to rely on multiple algorithms to accomplish a single task. This helps balance out strengths and weaknesses and minimize the impacts of data bias. Ensembling refers to the process of integrating and using different algorithms effectively and is an important feature to look for in ML platforms.

Competition Modeling

Since there is no way to know how an algorithm or ML model works before it’s deployed, it’s often necessary to conduct competition modeling. As the name implies, this pits multiple algorithms against each other to find out how accurate and valuable each is in predicting events. This leads to the selection of the best algorithms.

Deployment Tools

Putting an ML model into motion can involve numerous steps—and any error can result in subpar results or even failure. To prevent these kinds of issues, it’s important to ensure that an ML platform offers automation tools and, for some situations, one-click deployment. Many top-tier tools also offer both experimental and production-focused deployment workflows and support.

Dashboards and Monitoring

It’s essential to have visibility into the machine learning model’s performance and how it works, including the algorithms that are running and how they are evolving to meet new needs over time. Dashboards and monitoring tools are particularly effective in this area, especially if they come with filters and visual elements that help all stakeholders review important data. Having this kind of visibility helps an organization add, subtract, and change ML models as needed.

Also see: Top Data Visualization Tools

Benefits of Machine Learning Platforms

Organizations that use machine learning platforms to develop their ML models can create models on a greater scale, at a greater speed, and with higher levels of accuracy and utility. Some of the most common benefits that come from using machine learning platforms include the following:

• End-to-end ML: Many platforms take an end-to-end approach and give you all the tools you need to manage the full ML development and deployment lifecycle.
• ML model organization: The unified platform makes it easier to organize, find, and retrieve new and old ML models.
• Flexibility and extensibility: Users can work with various frameworks, software libraries, and programming languages to produce a model that fits their needs.
• Features for ease of use: Low-code/no-code tools are often available to simplify model development, deployment, and monitoring.
• Automation capabilities: Automation workflows can be set up for various areas of the ML lifecycle, simplifying, standardizing, and speeding up the entire process.
• Scalable platform capabilities: Several platforms work with big-data ML training sets and goals, including for large language models.
• Governance and ethical considerations: A growing number of ML vendors are incorporating model governance, cybersecurity, and other responsible frameworks into their platforms to make ML modeling a more ethical and manageable process.

Also see: Data Mining Techniques

How to Choose the Best Machine Learning Software

While it’s possible to build a custom ML system, most organizations rely on a dedicated machine learning platform from an ML, data science, or data analytics vendor. It’s best to evaluate your organization’s needs, including the type of machine-learning technology you require, before making your selection. Consider whether your organization would benefit from a classical method or deep learning approach, what programming languages are needed, and which hardware, software, and cloud services are necessary to deploy and scale a model effectively.

Another of the most important decisions you can make revolves around the underlying machine learning frameworks and libraries you choose. There are four main options to consider in this area:

• TensorFlow: An open-source and highly modular framework created by Google.
• PyTorch: A more intuitive open-source framework that incorporates Torch and Caffe2 and integrates with Python.
• scikit-learn: A user-friendly and highly flexible open-source framework that delivers sophisticated functionality.
• H2O: An open-source ML framework that’s heavily slanted to decision support and risk analysis.

Other key factors to consider when choosing an ML platform include available data ingestion methods, built-in design tools, version control capabilities, automation features, collaboration and sharing capabilities, templates and tools for building and testing algorithms, and the quantity and variety of compute resources.

Throughout the selection process, keep in mind that most of today’s platforms offer their solutions within a platform-as-a-service (PaaS) framework that includes cloud-based machine learning software and processing along with data storage and other tools and components. Pay close attention to how much support is offered through this model and if any community-driven support or training opportunities are included to help you get started.

Also see: Top AI Software

Review Methodology

The platforms in this machine learning platform review were assessed through a combination of multiple research techniques: combing through user reviews and ratings, reading whitepapers and product sheets, considering the range of common and differentiating features listed on product pages, and researching how each tool compares across a few key metrics. More than 25 platforms were assessed before we narrowed our list to these top players.

eWeek chose the top 10 selections in this list based on how well they addressed key feature requirements in areas like advanced data processing and management, feature engineering, model training and fine-tuning, performance monitoring, and reporting and analytics.

Beyond key features, we also considered how well each tool would meet the needs of a wide range of enterprise user audiences, whether your primary user is an experienced ML developer or data scientist or a non-technical team member who needs low-code model-building solutions. Finally, we looked at the affordability and scalability of each tool.

Bottom Line: Selecting the Best Machine Learning Solution for Your Business

The right ML solution for your business may end up being a combination of multiple solutions, as different platforms bring different strengths to the table. Some of these tools particularly excel at preparing data for high-quality model development. Others provide the frameworks and integrations necessary to build the model. Still others offer recommendations and managed support to help you optimize existing models for future performance goals.

With so many of these tools not only integrating well with each other but also available in free and/or open-source formats, it may well be worth the time it would take to incorporate multiple of these leading tools into your existing machine-learning development strategies.

Read next: Top 9 Generative AI Applications and Tools

The post 10 Best Machine Learning Platforms appeared first on eWEEK.

Topics we covered: 

• Let’s talk about the automating of data pipelines. What exactly does it mean for companies, and what are the challenges here?
• How do you recommend companies address these challenges with data pipelines and artificial intelligence?
• How is Ascend addressing the data pipeline needs of its clients?
• The future of data pipeline automation? What do you predict for the sector in the next 1-3 years?

Listen to the podcast:

Also available on Apple Podcasts

Watch the video:

The post Ascend.io CEO Sean Knapp on Automating Data Pipelines appeared first on eWEEK.

Open source intelligence is sourced from all corners of the web, and while that makes the data incredibly comprehensive, it also brings forth a large body of data that needs to be fact-checked and reviewed closely for the best possible results.

Let’s take a closer look at what open-source intelligence is, how it works, and how you can apply this type of intelligence to your business operations most effectively.

What Is Open Source Intelligence?

Open source intelligence is a type of data-driven intelligence that scours the internet and other public sources for information that’s relevant to a user’s query or search. Most often, OSINT is used to strategically collect information about a particular individual, group of people, organization, or other public entity.

Historically, OSINT developed before the internet and was a military espionage technique for finding relevant information about military enemies in newspapers, radio broadcasts, and other public data sources. While most data sources used for OSINT today are online or somehow digitized, OSINT analysts still have the option to collect physical data from public, open sources.

Also see: Top Data Visualization Tools

Passive vs. Active OSINT

Passive and active OSINT are both viable open source intelligence collection methods with different amounts of hands-on activity and in-depth research required.

With passive OSINT, users most often complete a simple search engine, social media, or file search or look at a website’s or news site’s homepage through a broad lens. They aren’t actively trying to collect highly specific information but rather are unobtrusively looking at the easiest-to-find, top-of-the-stack intelligence available. With this intelligence collection method, the goal is often to collect useful information without alerting targets or data sources to your intelligence collection activities.

When practicing active OSINT, the methods tend to be more intrusive and involved. Users may complete more complex queries to collect obscure intelligence and metadata from databases and network infrastructure, for example. They also might fill out a form or pay to get through a paywall for more information.

In some cases, active OSINT may even involve reaching out directly to sources for more information that is not publicly available or visible. While active OSINT is more likely to give users real-time, in-depth information than passive OSINT, it is much more difficult to do covertly and may lead you to legal troubles if your data collection methods aren’t careful.

Open Source Intelligence Data Sources

Open source intelligence can be sourced from any public dataset or property. These are some of the most common OSINT data sources from across the web:

• Social media platforms
• Public-facing websites
• News media
• Academic and scientific studies
• Internet of Things databases
• Business directories
• Financial reports
• Images and image libraries
• Public records, both digital and physical

Also see: Best Data Analytics Tools 

How Does Open Source Intelligence Work?

For individuals and organizations that want to take advantage of open source intelligence, a simple way to get started is with a search engine query. Often, asking the right question about the demographic information you need is the first step to finding relevant open source data entries that can lead to more detailed information.

Beyond using search engines for internet-wide data searches, you can also refine and focus your search on specific data platforms or databases, such as a certain social media platform. Depending on your goals and experience, you may also benefit from analyzing open source threat intelligence feeds and other sources that frequently update massive amounts of data.

If your data collection and analysis goals require you to work with big data sources like databases, data lakes, or live feeds, manual searches and research are ineffective. To quickly process and sort through large amounts of intelligence, you’ll want to consider investing in a web scraping or specialized OSINT tool that can automate and speed up the data analysis process.

OSINT Use Cases

Have you ever “Facebook stalked” someone you just met or Google searched your family’s last name to see what pops up? Both of these are simple examples of how even individuals practice a simplified form of open source intelligence in their daily lives.

Businesses, too, may collect OSINT without realizing it, but in most cases, they are collecting this kind of intelligence for a distinct competitive advantage or cause. Here are some of the most common OSINT use cases in practice today:

• Threat intelligence, vulnerability management, and penetration testing: Especially when used in combination with more comprehensive threat intelligence platforms, open source intelligence and data collection can give security analysts and professionals a more comprehensive picture of their threat landscape, any notable threat actors, and historical context for past vulnerabilities and attacks.
• Market research and brand monitoring: If you want to get a better look at both quantitative purchase histories and overall brand sentiment from customers, OSINT is an effective way to collect broad demographic intelligence about how your brand is performing in the eyes of the consumer. For this particular use case, you may conduct either passive or active OSINT in social media platforms, user forums, CRMs, chat logs, or other datasets with customer information.
• Competitive analysis: In a different version of the example above, you can complete OSINT searches on competitor(s) to learn more about how they’re performing in the eyes of customers.
• Geolocation data sourcing and analysis: Publicly available location data, especially related to video and image files, can be used to find an individual and/or to verify the accuracy of an image or video.
• Real-time demographic analyses over large populations: When large groups of people are participating in or enduring a major event, like an election cycle or a natural disaster, OSINT can be used to review dozens of social media posts, forum posts, and other consumer-driven data sources to get a more comprehensive idea of how people feel and where support efforts — like counterterrorism or disaster relief response, for example — may be needed.
• Background checks and law enforcement: While most law enforcement officials rely on closed-source, higher intelligence feeds for background checks and identification checks, OSINT sources can help fill in the blanks, especially for civilians who want or need to learn more about a person. Keep in mind that there are legal limits to how open source intelligence can be used to discriminate in hiring practices.
• Fact-checking: Journalists, researchers, and everyday consumers frequently use OSINT to quickly check multiple sources for verifiable information about contentious or new events. For journalistic integrity and ethical practice, it’s important to collect information directly from your sources whenever possible, though OSINT sources can be a great supplement in many cases.

Also read: Generative AI: 15 Enterprise Use Cases You Can Implement

10 OSINT Tools and Examples

Particularly for passive OSINT and simple queries, a web scraping tool or specialized “dork” query may be all that you need. But if you’re looking to collect intelligence on a grander scale or from more complex sources, consider getting started with one or several of the following OSINT tools:

1. Spyse: An internet asset registry that is particularly useful for cybersecurity professionals who need to find data about various threat vectors and vulnerabilities. It is most commonly used to support pentesting.
2. TinEye: A reverse image search engine that uses advanced image identification technology to deliver intelligence results.
3. SpiderFoot: An automated querying tool and OSINT framework that can quickly collect intelligence from dozens of public sources simultaneously.
4. Maltego: A Java-based cyber investigation platform that includes graphical link analysis, data mining, data merging, and data mapping capabilities.
5. BuiltWith: A tool for examining websites and public e-commerce listings.
6. theHarvester: A command-line Kali Linux tool for collecting demographic information, subdomain names, virtual host information, and more.
7. FOCA: Open source software for examining websites for corrupted documents and metadata.
8. Recon-ng: A command-line reconnaissance tool that’s written in Python.
9. OSINT Framework: Less of a tool and more of a collection of different free OSINT tools and resources. It’s focused on cybersecurity, but other types of information are also available.
10. Various data analysis and AI tools: A range of open source and closed source data analysis and AI tools can be used to scale, automate, and speed up the process of collecting and deriving meaningful insights from OSINT. Generative AI tools in particular have proven their efficacy for sentiment analysis and more complex intelligence collection methods.

More on a similar topic: Top 9 Generative AI Applications and Tools

Pros and Cons of Open Source Intelligence

Pros of OSINT

• Optimized cyber defenses: Improved risk mitigation and greater visibility into common attack vectors; hackers sometimes use OSINT for their own intelligence, so using OSINT for cyber defense is often an effective response.
• Affordable and accessible tools: OSINT data collection methods and tools are highly accessible and often free.
• Democratized data collection: You don’t need to be a tech expert to find and benefit from this type of publicly available, open source data; it is a democratized collection of valuable data sources.
• Quick and scalable data collection methods: A range of passive and active data sourcing methods can be used to obtain relevant results quickly and at scale.
• Compatibility with threat intelligence tools and cybersecurity programs: OSINT alone isn’t likely to give cybersecurity professionals all of the data they need to respond to security threats, but it is valuable data that can be fed into and easily combined with existing data sources and cybersecurity platforms.

Cons of OSINT

• Accessible to bad actors and hackers: Just like your organization can easily find and use OSINT, bad actors can use this data to find vulnerabilities and possible attack vectors. They can also use OSINT-based knowledge to disrupt and alter intelligence for enterprise OSINT activity.
• Limitations and inaccuracies: Public information sources rarely have extensive fact-checking or approval processes embedded into the intelligence collection process. Especially if multiple data sources share conflicting, inaccurate, or outdated information, researchers may accidentally apply misinformation to the work they’re doing.
• User error and phishing: Users may unknowingly expose their data to public sources, especially if they fall victim to a phishing attack. This means anyone from your customers to your employees could unintentionally expose sensitive information to unauthorized users, essentially turning that private information into public information.
• Massive amounts of data to process and review: Massive databases, websites, and social media platforms may have millions of data points that you need to review, and in many cases, those numbers are constantly growing and changing. It can be difficult to keep up with this quantity of data and sift through it to find the most important bits of intelligence.
• Ethical and privacy concerns: OSINT is frequently connected without the target’s knowledge, which is an issue with AI and ethics. Depending on the data source and sourcing method, this information can be used to harm or manipulate people, especially when it’s PII or PHI that has accidentally been exposed to public view.

Bottom Line: Using OSINT for Enterprise Threat Intelligence

Getting started with open source intelligence can be as simple as conducting a Google search about the parties in question. It can also be as complex as sorting through a publicly available big data store with hundreds of thousands of data entries on different topics.

Regardless of whether you decide to take a passive or active approach, make sure all members of your team are aware of the goals you have in mind with open source intelligence work and, more importantly, how they can collect that intelligence in a standardized and ethical manner.

Read next: 50 Generative AI Startups to Watch in 2023

The post Open Source Intelligence (OSINT) Guide appeared first on eWEEK.

Among the topics we discussed:

• As small and medium-sized businesses grapple with data management challenges, what issues do you see?
• How do you recommend addressing these challenges? What role can the cloud play?
• How is AWS addressing the SMB market in particular?
• The future of data management and the cloud? How can businesses prepare now for future changes?

Listen to the podcast:

Also available on Apple Podcast

Watch the video:

The post AWS’s Ben Schreiner’s on Data Management for SMBs appeared first on eWEEK.

A data lake is a centralized repository that allows storage of large amounts of data in its native, raw format. On the other hand, a data warehouse is a repository that stores structured and semi-structured data from multiple sources for analysis and reporting purposes.

A data lakehouse aims to bridge the gap between these two data management approaches by merging the flexibility, scale and low cost of data lake with the performance and ACID (Atomicity, Consistency, Isolation, Durability) transactions of data warehouses. This enables business intelligence and analytics on all data in a single platform.

Jump to:

• What does a data lakehouse do?
• Difference between data lakehouse, data warehouse, and data lake
• 5 layers of data lakehouse architecture
• Key features of a data lakehouse
• Advantages of a data lakehouse
• Challenges of a data lakehouse
• Bottom line: the data lakehouse

What Does a Data Lakehouse Do? 

A data lakehouse leverages a data repository’s scalability, flexibility and cost-effectiveness, allowing organizations to ingest vast amounts of data without imposing strict schema or format requirements.

In contrast with data lakehouses, data lakes alone lack the governance, organization, and performance capabilities needed for analytics and reporting.

Data lakehouses also are distinct from data warehouses. Data warehouses use extract, load and transform (ELT), or alternatively use extract, transform, and load (ETL) processes to load structured data into a relational database infrastructure – a data warehouse supports enterprise data analytics and business intelligence applications. However, a data warehouse is limited by its inefficiency in handling unstructured and semi-structured data. Additionally, they can get costly as data sources and quantity grow over time.

Data lakehouses address the limitations and challenges of both data warehouses and data lakes by integrating the flexibility and cost-effectiveness of data lakes with data warehouses’ governance, organization, and performance capabilities.

The following users can leverage a data lakehouse:

• Data scientists can use a data lakehouse for machine learning, BI, SQL analytics and data science.
• Business analysts can leverage it to explore and analyze diverse data sources and business uses.
• Product managers, marketing professionals, and executives can use data lakehouses to monitor key performance indicators and trends.

Also see: What is Data Analytics

Deeper Dive: Data Lakehouse vs. Data Warehouse and Data Lake

We have established that data lakehouse is a product of data warehouse and data lake capabilities. It enables efficient and highly flexible data ingestion. Let’s take a deeper look at how they compare.

Data warehouse

The data warehouse is the “house” in a data lakehouse. A data warehouse is a type of data management system specially designed for data analytics; it facilitates and supports business intelligence (BI) activities. A typical data warehouse includes several elements, such as:

• A relational database.
• An ELT solution for preparing the data for analysis, statistical analysis, reporting, and data mining capabilities.
• A client analysis tools for data visualization.

Data lake

A data lake is the “lake” in a data lakehouse. A data lake is a flexible, centralized storage repository that allows you to store all your structured, semi-structured and unstructured data at any scale. A data lake uses a schema-on-read methodology, meaning there is no predefined schema into which data must be fitted before storage.

This chart compares data lakehouse vs. data warehouse vs. data lake concepts.

5 Layers of Data Lakehouse Architecture

The IT architecture of the data lakehouse consists of five layers, as follows:

Ingestion layer

Data ingestion is the first layer in the data lakehouse architecture. This layer collects data from various sources and delivers it to the storage layer or data processing system. The ingestion layer can use different protocols to connect internal and external sources, such as:

• Database management systems
• Software as a Service (SaaS) applications
• NoSQL databases
• Social media
• CRM applications
• IoT sensors
• File systems

The ingestion layer can perform data extraction in a single, large batch or small bits, depending on the source and size of the data.

​​Storage layer

The data lakehouse storage layer accepts all data types as objects in affordable object stores like AWS S3.

This layer stores structured, unstructured, and semi-structured data in open source file formats like Parquet or Optimized Row Columnar (ORC). A data lakehouse can be implemented on-premise using a distributed file system like Hadoop Distributed File System (HDFS) or cloud-based storage services like Amazon S3.

Also see: Top Data Analytics Software and Tools 

Metadata layer

This layer is very important because it serves as the origin of the data lakehouse. Metadata is data that provides information about other data pieces – in this layer, it’s a unified catalog that includes metadata for data lake objects. The metadata layer also equips users with a range of management functionalities, such as:

• ACID (Atomicity, Consistency, Isolation, Durability) transactions ensure atomicity, consistency, isolation, and durability for data modifications.
• File caching capabilities optimize data access by keeping frequently accessed files readily available in memory.
• Indexing accelerates queries by enabling swift data retrieval.
• Data versioning enables users to save specific versions of the data.

The metadata layer empowers users to implement predefined schemas to enhance data governance and enable access control and auditing capabilities.

API layer

The API layer is a particularly important component of a data lakehouse. It allows data engineers, data scientists, and analysts to access and manipulate the data stored in the data lakehouse for analytics, reporting, and other use cases.

Consumption layer

The consumption layer is the final layer of data lakehouse architecture – it is used to host tools and applications such as Power BI and Tableau, enabling users to query, analyze, and process the data. The consumption layer allows users to access and consume the data stored in the data lakehouse for various business use cases.

Key Features of a Data Lakehouse

• ACID transaction support: Many data lakehouses use a technology like Delta Lake (developed by Databricks) or implement ACID transactions to provide data consistency and reliability in a distributed environment.
• Single data low-cost data storage: Data lakehouse is a cost-effective option for storing all data types, including structured, semi-structured and unstructured data.
• Unstructured and streaming data support: While a data warehouse is limited to structured data, a data lakehouse supports many data formats, including video, audio, text documents, PDF files, system logs and more. A data lakehouse also supports real-time ingestion of data – and streaming from devices.
• Open formats support: Data lakehouses can store data in standardized file formats like Apache Avro, Parquet and ORC (Optimized Row Columnar).

Advantages of a Data Lakehouse

A data lakehouse offers many benefits, making it a worthy alternative solution to a standalone data warehouse or data lake. Data lakehouses combine the quality service and performance of a data warehouse with the affordability and flexible storage infrastructure of a data lake. Data lakehouse helps data users solve the following issues.

• Unified data platform: It serves as a structured and unstructured data repository, eliminating data silos.
• Real-time and batch processing: Data lakehouses support real-time processing for fast and immediate insight and batch processing for large-scale analysis and reporting.
• Reduced cost: Maintaining a separate data warehouse and data lake can be too pricey. With a data lakehouse, data management teams only have to deploy and manage one data platform.
• Better data governance: Data lakehouses consolidate resources and data sources, allowing greater control over security, metrics, role-based access, and other crucial management elements.
• Reduced data duplication: When copies of the same data exist in disparate systems, it is more likely to be inconsistent and less trustworthy. Data lakehouses provide organizations with a single data source that can be shared across the business, preventing any inconsistencies and extra storage costs caused by data duplication.

Challenges of a Data Lakehouse

A data lakehouse isn’t a silver bullet to address all your data-related challenges. The data lakehouse concept is relatively new and its full potential and capabilities are still being explored and understood.

A data lakehouse is a complex system to build from the ground up. You’ll need to either opt for an out-of-box data lakehouse solution whose performance is highly variable, depending on the query type and the engine processing it, or invest time and resources to develop and maintain your custom solution.

Bottom Line: the Data Lakehouse

The data lakehouse is a new concept that represents a modern approach to data management. It’s not an outright replacement for the traditional data warehouse or data lake but a combination of both.

Although data lakehouses offer many advantages that make it desirable, it is not foolproof. You must take proactive steps to avoid and manage the security risks, complexity, as well as data quality and governance issues that may arise while using a data lakehouse system. 

Also see: Generative AI and Data Analytics Best Practices 

The post What is a Data Lakehouse? Definition, Benefits & Features appeared first on eWEEK.

In short, Snowflake is more suited for standard data transformation and analysis and for those users familiar with SQL. Databricks is geared for streaming, ML, AI, and data science workloads courtesy of its Spark engine, which enables the use of multiple development languages.

Both Snowflake and Databricks provide the volume, speed, and quality demanded by business intelligence applications. But there are as many similarities as there are differences. When examined closely, it becomes clear that these two cloud-based data platforms have a different orientation. Therefore, selection often boils down to tool preference and suitability for the organization’s data strategy.

What Is Snowflake?

Snowflake is a major cloud company that focuses on data-as-a-service features and functions for big data operations. Its core platform is designed to seamlessly integrate data from various business apps and in different formats in a unified data store. Consequently, typical extract, transform, and load (ETL) operations may not be necessary to get the data integration results you need.

The platform is compatible with various types of business workloads, including artificial intelligence and machine learning, data lakes and data warehouses, and cybersecurity workloads. It is ideally designed for organizations that are working with large quantities of data that require precise data governance and management systems in place.

What Is Databricks?

Databricks is a data-driven vendor with products and services that focus on data lake and warehouse development as well as AI-driven analytics and automation. Its flagship lakehouse platform includes unified analytics and AI management features, data sharing and governance capabilities, AI and machine learning, and data warehousing and engineering.

Users can access certain platform features through an open-source format, making this a highly extensible and customizable solution for developers. It’s also a popular solution for users who want to incorporate other AI or IDE integrations into their setup.

Snowflake vs. Databricks: Comparing Key Features

We’ll compare these two data companies in greater detail in the sections to come, but for a quick scan, we’ve developed this table to compare Snowflake vs. Databricks across a few key metrics and categories:

Snowflake is a relational database management system and analytics data warehouse for structured and semi-structured data.

Offered via the software-as-a-service (SaaS) model, Snowflake uses an SQL database engine to manage how information is stored in the database. It can process queries against virtual warehouses within the overall warehouse, each one in its own cluster node independent of others so as not to share compute resources.

Sitting on top of that database engine are cloud services for authentication, infrastructure management, queries, and access controls. The Snowflake Elastic Data Warehouse enables users to analyze and store data utilizing Amazon S3 or Azure resources.

Databricks is also cloud-based but is based on Apache Spark. Its management layer is built around Apache Spark’s distributed computing framework to make infrastructure management easier. Databricks positions itself as a data lake rather than a data warehouse. Thus, the emphasis is more on use cases such as streaming, machine learning, and data science-based analytics.

Databricks can be used to handle raw unprocessed data in large volumes. Databricks is delivered as SaaS and can run on AWS, Azure, and Google Cloud. There is a data plane as well as a control plane for backend services that delivers instant compute. Its query engine is said to offer high performance via a caching layer. Snowflake includes a storage layer while Databricks provides storage by running on top of AWS S3, Azure Blob Storage, and Google Cloud Storage.

For those wanting a top-class data warehouse, Snowflake wins. But for those needing more robust ELT, data science, and machine learning features, Databricks is the winner.

Snowflake vs. Databricks: Support and Ease of Use Comparison

The Snowflake data warehouse is said to be user-friendly, with an intuitive SQL interface that makes it easy to get set up and running. It also has plenty of automation features to facilitate ease of use. Auto-scaling and auto-suspend, for example, help in stopping and starting clusters during idle or peak periods. Clusters can be resized easily.

Databricks, too, has auto-scaling for clusters. The UI is more complex for more arbitrary clusters and tools, but the Databricks SQL Warehouse uses a straightforward “t-shirt sizing approach” for clusters that makes it a user-friendly solution as well. 

Both tools emphasize ease of use in certain capacities, but Databricks is intended for a more technical audience, so certain steps like updating configurations and switching options may involve a steeper learning curve.

Both Snowflake and Databricks offer online, 24/7 support, and both have received high praise from customers in this area.

Though both are top players in this category, Snowflake wins for its wider range of user-friendly and democratized features.

Also see: Top Business Intelligence Software

Snowflake vs. Databricks: Security Comparison

Snowflake and Databricks both provide role-based access control (RBAC) and automatic encryption. Snowflake adds network isolation and other robust security features in tiers with each higher tier costing more. But on the plus side, you don’t end up paying for security features you don’t need or want.

Databricks, too, includes plenty of valuable security features. Both data vendors comply with SOC 2 Type II, ISO 27001, HIPAA, GDPR, and more.

No clear winner in this category.

Snowflake vs. Databricks: Integrations Comparison

Snowflake is on the AWS Marketplace but is not deeply embedded within the AWS ecosystem. In some cases, it can be challenging to pair Snowflake with other tools. But in other cases, Snowflake is wonderfully integrated. Apache Spark, IBM Cognos, Tableau, and Qlik are all fully integrated. Those using these tools will find analysis easy to accomplish.

Both tools support semi-structured and structured data. Databricks has more versatility in terms of supporting any format of data, including unstructured data. Snowflake is adding support for unstructured data now, too.

Databricks wins this category.

Also see: Top Data Mining Tools 

Snowflake vs. Databricks: AI Features Comparison

Both Snowflake and Databricks include a range of AI and AI-supported features in their portfolio, and the number only seems to grow as both vendors adopt generative AI and other advanced AI and ML capabilities.

Snowflake supports a range of AI and ML workloads, and in more recent years has added the following two AI-driven solutions to its portfolio: Snowpark and Streamlit. Snowpark offers users several libraries, runtimes, and APIs that are useful for ML and AI training as well as MLOps. Streamlit, now in public preview, can be used to build a variety of model types — including ML models — with Snowflake data and Python development best practices.

Databricks, on the other hand, has more heavily intertwined AI in all of its products and services and for a longer time. The platform includes highly accessible machine learning runtime clusters and frameworks, autoML for code generation, MLflow and a managed version of MLflow, model performance monitoring and AI governance, and tools to develop and manage generative AI and large language models.

While both vendors are making major strides in AI, Databricks takes the win here.

Snowflake vs. Databricks: Price Comparison

There is a great deal of difference in how these tools are priced. But speaking very generally: Databricks is priced at around $99 a month. There is also a free version. Snowflake works out at about $40 a month, though it isn’t as simple as that.

Snowflake keeps compute and storage separate in its pricing structure. And its pricing is complex with five different editions from basic up, and prices rise as you move up the tiers. Pricing will vary tremendously depending on the workload and the tier involved.

As storage is not included in its pricing, Databricks may work out cheaper for some users. It all depends on the way the storage is used and the frequency of use. Compute pricing for Databricks is also tiered and charged per unit of processing. The differences between them make it difficult to do a full apples-to-apples comparison. Users are advised to assess the resources they expect to need to support their forecast data volume, amount of processing, and their analysis requirements. For some users, Databricks will be cheaper, but for others, Snowflake will come out ahead.

This is a close one as it varies from use case to use case.

Also see: Real-Time Data Management Trends

Snowflake and Databricks Alternatives

Bottom Line: Snowflake vs. Databricks

Snowflake and Databricks are both excellent data platforms for data analysis purposes. Each has its pros and cons. Choosing the best platform for your business comes down to usage patterns, data volumes, workloads, and data strategies.

Snowflake is more suited for standard data transformation and analysis and for those users familiar with SQL. Databricks is more suited to streaming, ML, AI, and data science workloads courtesy of its Spark engine, which enables the use of multiple development languages. Snowflake has been playing catchup on languages and recently added support for Python, Java, and Scala.

Some say Snowflake is better for interactive queries as it optimizes storage at the time of ingestion. It also excels at handling BI workloads, and the production of reports and dashboards. As a data warehouse, it offers good performance. Some users note, though, that it struggles when faced with huge data volumes as would be found with streaming workloads. In a straight competition on data warehousing capabilities, Snowflake wins.

But Databricks isn’t really a data warehouse at all. Its data platform is wider in scope with better capabilities than Snowflake for ELT, data science, and machine learning. Users store data in managed object storage of their choice. It focuses on the data lake and data processing. But it is squarely aimed at data scientists and professional data analysts.

In summary, Databricks wins for a technical audience. Snowflake is highly accessible to a technical and less technical user base. Databricks provides pretty much every data management feature offered by Snowflake and a lot more. But it isn’t quite as easy to use, has a steeper learning curve, and requires more maintenance. Regardless though, Databricks can address a much wider set of data workloads and languages, and those familiar with Apache Spark will tend to gravitate toward Databricks.

Snowflake is better set up for users who want to deploy a good data warehouse and analytics tool rapidly without bogging down in configurations, data science minutia, or manual setup. But this isn’t to say that Snowflake is a light tool or for beginners. Far from it. 

But it isn’t high-end like Databricks, which is aimed more at complex data engineering, ETL, data science, and streaming workloads. Snowflake, in contrast, is a warehouse to store production data for analytics purposes. It is accessible for beginners, too, and for those who want to start small and scale up gradually.

Pricing comes into the selection picture, of course. Sometimes Databricks will be much cheaper due to the way it allows users to take care of their own storage. But not always. Sometimes Snowflake will pan out cheaper.

The post Snowflake vs. Databricks: Comparing Cloud Data Platforms appeared first on eWEEK.

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