Azure Synapse vs Databricks: Data Platform Comparison
Obviously, Microsoft Azure Synapse and Databricks are highly respected data platforms. They each deliver the volume, speed and quality demanded by leading data analytics and business intelligence solutions.
And both data platforms meet an urgent need. Data analysis and management has become more important than ever in the modern business world. With the ever-increasing volume of data to analyze, organizations need a way to bring all that data together in one place where it’s ripe for data mining.
Comparing Microsoft Azure Synapse and Databricks is a complex task. In many cases, the choice comes down to the specific data management needs of the environment. Let’s take a look at these two data platforms and see which one stands out.
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Azure Synapse vs. Databricks: Key Features Comparison
Azure Synapse was formerly known as Microsoft Azure SQL Data Warehouse. It is built on a solid SQL foundation and aims to be a unified data analytics platform for big data systems and data warehouses.
Its massively parallel processing architecture is designed so that its fast processing is not entirely dependent on expensive memory (unlike Databricks). It achieves this by using clustered and nonclustered indexes and column storage segments that make it easier to determine where data is stored and how it is distributed.
Synapse has tight integration with many other Azure tools. Its Purview data cataloging system, for example, is used for data governance. This makes it easier to transform, curate, and cleanse the data before it is distributed to other users for analysis. It also makes it relatively simple to track data lineage, refer to the schema of tables, and track the movement of data through the system.
Databricks is also cloud-based but is based on Apache Spark. Its management layer is built around the distributed computing framework of Apache Spark to facilitate infrastructure management. It uses a batch streaming data processing engine for distribution across multiple nodes.
Databricks positions itself more as a data lake than a data warehouse. Thus, the focus is more on use cases such as streaming, machine learning, and data science-based analytics. It can be used to handle large volume unprocessed raw data.
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 core services that provide instant computing. Its query engine is supposed to deliver high performance through a caching layer. Databricks provides storage by running on AWS S3, Azure Blob Storage, and Google Cloud Storage.
For those who want a top-notch data warehouse for analytics, Azure Synapse wins. But for those who need more robust ELT functionality (extract, load, transform), data science, and machine learning, Databricks is the winner.
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Azure Synapse vs. Databricks: Support, Usability Comparison
Synapse’s reliance on SQL and Azure provides familiarity to the many companies and developers using these platforms around the world. For them, it is easy to use. Similarly, Databricks is perfect for those familiar with Apache tools. But Databricks takes a data science approach, using open source and machine libraries, which may be difficult for some users.
Databricks can run Python, Spark Scholar, SQL, NC SQL, and other platforms. It comes with its own user interface as well as ways to connect to endpoints like JDBC connectors. Some users, however, report that it may seem complex and unfriendly as it is aimed at a technical market and requires more manual input for cluster resizing or configuration updates. There can be a steep learning curve for some.
Azure Synapse wins.
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Azure Synapse vs. Databricks: Comparison Security
Azure Synapse provides data protection, access control, authentication, network security, and threat protection to identify unusual access locations, SQL injection attacks, and authentication attacks. Other safety features include component isolation limits.
Databricks also provided role-based access control (RBAC), automatic encryption, and many other security features. Both platforms do a good job of security, so there’s no clear winner in this category.
Azure Synapse vs. Databricks: Integration Comparison
Microsoft has taken its traditional Azure SQL Data Warehouse and integrated integration components such as Data Factory for ETO and ELT data movement, as well as Power BI for analytics. Synapse even offers Spark components like Azure Spark Pools to run notebooks. Synapse works seamlessly with all other Azure tools.
In comparison, Databricks requires third-party tools and API configurations to integrate data governance and lineage features, which are more seamlessly integrated into Azure Synapse courtesy of Purview. Databricks, however, supports all data formats, including unstructured data.
Azure Synapse narrowly wins.
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Azure Synapse vs. Databricks: Price comparison
There is a big difference in the price of these tools. But very generally speaking: Databricks costs around $99 per month. There is also a free version. Since storage is not included in its price, Databricks may be cheaper for some users. It all depends on how the storage is used and how often it is used. Compute pricing for Databricks is also tiered and billed per processing unit.
When it comes to Azure Synapse too, things get even more complex. It is billed based on the number of data warehouse blocks and hours of execution, the amount of TB stored and processed, the number of Apache Spark Pool instances running, and the number of hours, orchestration activity execution volume, data movement, execution environment, and cores used in executing and debugging dataflows.
The differences between them make a comprehensive apples-to-apples comparison difficult. Users are advised to assess the resources they expect to need to support their forecast data volume, amount of processing, and analysis requirements. For some users Databricks will be cheaper, for others Azure Synapse will come out on top.
This is close as it varies from use case to use case. But because its pricing system is a bit less complex, Databricks wins.
See also: Trends in real-time data management
Azure Synapse vs. Databricks: Conclusion
Azure Synapse and Databricks are great data warehouses/platforms for analytics. Each has advantages and disadvantages. It all depends on usage patterns, data volumes, workloads, and data strategies.
Azure Synapse is best suited for data analysis and users familiar with SQL.
Databricks is more suitable for streaming, ML, AI, and data science workloads thanks to its Spark engine, which allows the use of multiple languages. It’s not really a data warehouse. Its data platform has a broader reach with better capabilities than Azure Synapse for ELT, data science, and machine learning. Users store data in the managed object storage of their choice and this is not included in its pricing. It focuses on the data lake and data processing. But it is aimed directly at data scientists and highly skilled analysts.
In summary, Databricks wins for a technical audience. Azure Synapse wins for a less technically savvy user base. Databricks provides much of the data management functionality offered by Azure Synapse. But it’s not as easy to use, has a steep learning curve, and requires more maintenance. But it can handle a wider set of data and language workloads. And those familiar with Apache Spark will tend to turn to Databricks.
Azure Synapse is best configured for users who simply want to quickly deploy a good data warehouse and analytics tool without getting bogged down in configurations, data science details, or manual configuration. Still, it cannot be categorized as a lightweight or beginner-only tool. Far from there. But it’s not high-end like Databricks, which is more aimed at complex data engineering, ETL, data science, and streaming workloads.
As such, its batch data processing engine tends to require significantly more memory than Azure Synapse. The fact that Databricks can run Python, Spark Scholar, SQL, NC SQL, etc. will certainly make it appealing to developers in these camps.
As usual, the comparison between these tools comes down to user preference for platform, programming language, and existing investment in vendor platforms or open source tools.