Clearly, both Microsoft Azure Synapse and Databricks are well-respected data platforms. They each provide the volume, speed, and quality demanded by leading data analytics and business intelligence solutions.
And both data platforms serve an urgent need. Data analytics and data management have become more important than ever in the modern business world. With the volume of data to be analyzed steadily rising, organizations need a way to corral all that data in one place, where it is ripe for data mining.
Comparing Microsoft Azure Synapse and Databricks is a complex task. In many cases, the choice boils down to the specific data management needs of the environment. Let’s examine both these data platforms and see which one comes out ahead.
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Azure Synapse vs. Databricks: Comparing Key Features
Azure Synapse used to be known as the Microsoft Azure SQL Data Warehouse. It is built on a strong SQL foundation and seeks to be a unified data analytics platform for big data systems and data warehouses.
Its massively parallel processing architecture is designed so that its rapid processing is not wholly reliant on expensive memory (unlike Databricks). It achieves this by using clustered and non-clustered column store indexes and segments that make it easier to determine where data is stored and how it is distributed.
Synapse benefits from tight integration with the many other Azure tools. Its Purview data cataloging system, for example, is used for data governance. This makes it easy to transform, curate, and cleanse data before it is distributed to other users for analytics. This also makes it relatively simple to track data lineage, refer to schema of tables, and track data movement through the system.
Databricks is also based in the cloud but is based on Apache Spark. Its management layer is built around Apache Spark’s distributed computing framework to make management of infrastructure easier. It uses a batch in-stream data processing engine for distribution across multiple nodes.
Databricks positions itself as more of a data lake than a data warehouse. Thus, the emphasis is more on use cases such as streaming, machine learning, and data science-based analytics. It can be used to handle raw unprocessed data in large volume.
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. 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 for analytics, Azure Synapse wins. But for those needing more robust ELT (extract, load, transform), data science, and machine learning features, Databricks is the winner.
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Azure Synapse vs. Databricks: Support, Ease of Use Comparison
Synapse’s reliance on SQL and Azure offers familiarity to the many companies and developers who use those platforms around the world. For them, it is easy to use. Similarly, Databricks is perfect for those used to Apache tools. But Databricks does take a data science approach, using open source and machine libraries, which may be challenging for some users.
Databricks can run Python, Spark Scholar, SQL, NC SQL, and other platforms. It comes packaged with its own user interface as well as ways to connect to endpoints such as JDBC connectors. Some users, though, report that it can appear complex and not user friendly as it is aimed at a technical market and needs more manual input for cluster resizing clusters or configuration updates. There may be a steep learning curve for some.
Azure Synapse wins.
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Azure Synapse vs. Databricks: Comparing Security
Azure Synapse offers data protection, access control, authentication, network security, and threat protection to identify unusual access locations, SQL injection attacks, and authentication attacks. Further security features include component isolation limits.
Databricks, too, provided role-based access control (RBAC) and automatic encryption and plenty of other security features. Both platforms do a good job of security so there is no clear winner in this category.
Azure Synapse vs. Databricks: Integration Comparison
Microsoft has taken its traditional Azure SQL Data Warehouse and baked in integration components such as Data Factory for ETO and ELT data movement, as well as Power BI for analytics. Synapse even features Spark components such as Azure Spark Pools in order to run notebooks. Synapse works seamlessly with all the other Azure tools.
In comparison, Databricks requires some third-party tools and API configurations to integrate governance and data lineage features, which are more seamlessly integrated in Azure Synapse courtesy of Purview. Databricks, however, supports any format of data including unstructured data.
Azure Synapse narrowly wins.
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Azure Synapse 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. 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.
When it comes to Azure Synapse, too, things get even more complex. It is charged according to the number of data warehouse blocks and the number of hours running, the amount of TB stored and processed, the number of instances of Apache Spark Pool running and the number of hours, the volume of orchestration activity runs, data movement, runtime, and cores used in data flow execution and debugging.
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, for others Azure Synapse will come out ahead.
This is a close one as it varies from use case to use case. But due to the fact that its pricing scheme is a little less complex, Databricks wins.
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Azure Synapse vs. Databricks: Conclusion
Azure Synapse and Databricks are excellent data warehouses/platforms for analysis purposes. Each has pros and cons. It all comes down to usage patterns, data volumes, workloads, and data strategies.
Azure Synapse is more suited for data 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 use of multiple languages. It isn’t really a data warehouse at all. Its data platform is wider in scope with better capabilities than Azure Synapse for ELT, data science, and machine learning. Users store data in managed object storage of their choice and this doesn’t get included in its pricing. It focuses on the data lake and data processing. But it is squarely aimed at data scientists and highly capable analysts.
In summary, Databricks wins for a technical audience. Azure Synapse wins for a less technical savvy user base. Databricks provides pretty much of the data management functionality offered by Azure Synapse. But it isn’t as easy to use, has a steep learning curve, and requires more maintenance. But it can address a wider set of data workloads and languages. And those familiar with Apache Spark will tend to gravitate toward Databricks.
Azure Synapse is better set up for users that just want to deploy a good data warehouse and analytics tool rapidly without bogging down in configurations, data science minutiae, or manual setup. Yet it can’t be classified as a light tool or for beginners only. 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.
As such, its batch data processing engine tends to require a lot more memory than Azure Synapse. The fact that Databricks can run Python, Spark Scholar, SQL, NC SQL, and more will certainly make it attractive to developers in those camps.
As usual, comparison between such tools comes down to user preference for platform, programming language, and existing investment in vendor platforms or open-source tools.