Drawing a comparison between top data platforms Snowflake and Databricks is crucial for today’s businesses because data analytics and data management are now deeply essential to their operations and opportunities for growth. Which data platform is best for your business?
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.
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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:
|Support and Ease of Use
|Dependent on Use Case
|Dependent on Use Case
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
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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.