What are the major pros and cons of using Synapse Spark Notebooks over Databricks Notebooks?

Question

I am using Synapse Notebooks for ingesting data via pipelines , making Delta Tables and ultimately aim to analyze the data.
Since Databricks notebooks can also be used for the above purposes and both have Spark.
I understand Synapse offers all analytics tools in one service , apart from this , are there any other pros(or cons) of using Synapse Spark Notebooks when compared to Databricks Spark notebooks.
Also , are there any major differences in the Spark engine in regards to the features they provide for Synapse and Databricks notebooks.

Answer 1

Hello @sachink ,

Thanks for the question and using MS Q&A platform.

Apache Spark on both platforms:

Azure Databricks:

• Industry leading Spark (Databricks Runtime) built on a highly optimized version of Apache Spark offering 50x performance
• Already has support for Spark 3.1.2 with DBR 8.4
• Allows users to opt for GPU enabled clusters and choose between standard and high-concurrency cluster mode
• Supports for steaming data

Azure Synapse:

• Open-source Apache Spark (thus not including all features of Databricks Runtime)
• Supports Apache Spark 2.4 (GA) and 3.0 (Preview)
• Has built-in support for .NET for Spark application.
• Unified security and monitoring features including Managed VNets.

Notebooks experience on both platforms:

Azure Databricks:

• Databricks Notebooks has as real-time co-authoring (both authors see the changes in real-time)
• Automated versioning with CI/CD

Azure Synapse:

• Nteract Notebooks has co-authoring of Notebooks, but one person needs to save the Notebook before another person sees the change.
• Automated versioning with CI/CD

Azure Synapse advantages over Azure Databricks:

• Azure Synapse Analytics is a limitless analytics service that brings together data integration, enterprise data warehousing and big data analytics.
• It gives you the freedom to query data on your terms, using either serverless or dedicated resources—at scale.
• Azure Synapse brings these worlds together with a unified experience to ingest, explore, prepare, manage and serve data for immediate BI and machine learning needs.

If you go with Modern analytics architecture with Azure Databricks, you need to manage ingest, explore, manage and serve separately.

For more details, refer to Analytics end-to-end with Azure Synapse and Modern analytics architecture with Azure Databricks

Hope this helps. Do let us know if you any further queries.

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