differnce between synapse and databricks

Question

What are major difference between synapse and data bricks
and under what circumstances one should use synapse and vice versa

Answer 1

Hello @Shivam Ramola !

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Databricks:

Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. Designed with the founders of Apache Spark, Databricks is integrated with Azure to provide one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.

For more information on Databricks, please visit here :- https://learn.microsoft.com/en-us/azure/databricks/

Synapse Analytics:

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

For more information on Synapse , please visit here :- https://learn.microsoft.com/en-us/azure/synapse-analytics/

they do overlap to some extent, but they are not the same thing. Databricks is pretty much managed Apache Spark, whereas Synapse Analytics is managed SQL Data Warehouse.

When to use Databricks and Synapse Analytics

Machine Learning development – preferred: Databricks

Has ML optimized Databricks runtimes which include some of the most popular libraries (e.g. TensorFlow, PyTorch, Keras etc.) and GPU enabled clusters
managed and hosted version of MLflow is provided in Databricks with integrated enterprise security and some other Databricks-only capabilities
you can use AzureML from Databricks
support for GPUs
tight version control integration (git) + CICD on full environments
Synapse
Built-in support for AzureML
You can use open-source MLflow
No full git experience or multi-user collaboration on notebook
No full CICD yet on environment & dependencies
Reflection: based on current available features, Databricks goes broader in ML features within Spark and gives a more comfortable developer experience (e.g. use of IDEs).

Ad-hoc data lake discovery – both Synapse & Databricks

Databricks – you can query data from the data lake by first mounting the data lake to your Databricks workspace and then use Python, Scala, R to read the data
Synapse – you can use the SQL on-demand pool or Spark in order to query data from your data lake
Reflection: we recommend to use the tool or UI you prefer. If you are a BI developer familiar with SQL & Synapse, Synapse is perfect; if you are a data scientists only using notebooks: use Databricks to discover your data lake.

Real-time transformations – preferred: Databricks

Databricks
Spark Structured Streaming as part of Databricks is proven to work seamlessly (has extra features as part of the Databricks Runtime e.g. Z-order clustering when using Delta, join optimizations etc.)
Autoloader – new functionality from Databricks allowing to incrementally
Synapse
As a data warehouse, we can ingest real-time data into Synapse using Stream analytics but this currently doesn’t support Delta. As a developer platform, Synapse doesn’t fully focus on real-time transformations yet.
Reflection: Use Databricks if you want to use Spark’s Structured Streaming (and thus advanced transformations) and load real-time data into your delta lake.

SQL Analyses & Data warehousing – preferred: Synapse

Synapse
A full data warehousing allowing to full relational data model, stored procedures, etc.
Provides all SQL features any BI-er has been used to incl. a full standard T-SQL experience
Brings together the best SQL technologies incl. columnar-indexing

Databricks
A delta-lake-based data warehouse is possible but not with the full width of SQL and data warehousing capabilities as a traditional data warehouse.
Databricks leverages the Delta Lakehouse paradigm offering core BI functionalities but a full SQL traditional BI data warehouse experience.
Doesn’t provide a full T-SQL experience (Spark SQL)
Reporting and self-service BI – preferred: Synapse
Synapse
You can use Power BI directly from Synapse Studio
The SQL pool (SQL DWH) is leader in enterprise data warehousing

Regards,
Tasadduq Burney

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