When to use Synapse Spark pool vs Azure Databricks ?

Question

Hi Team,

We already have a Synapse Analytics Workspace. My team is having ongoing discussion whether to use Synapse Spark Pool or Azure Databricks. The source will be some kind of Relational Database and we are really not sure what's the advantage of Databricks over Synapse Spark Pool as Spark pool has also notebooks in which we can switch languages as well. We are already having Azure DevOps configured for the purpose of CI/CD.

Can someone please tell the differences between Spark Pool and Azure Databricks?

Which one should I prefer?

Kind Regards,

Madhusudan

Answer 1

Hello MADHUSUDAN PANWAR,

Azure Synapse Spark Pool and Azure Databricks are big data processing platforms using Apache Spark. However, they have some differences mentioned below.

• Integration: Synapse Spark Pool is integrated into Azure Synapse Analytics, providing a unified analytical platform for working with big data. It seamlessly integrates with other Synapse services like SQL serverless pools and dedicated SQL pools. Azure Databricks is a standalone service that provides a collaborative environment for data engineering and data scientists.
• Environment: Synapse Spark Pool supports autoscale, dynamic allocation, and automatic pause features, which can help optimize resource usage and reduce costs. Azure Databricks provides autoscaling and job clusters for optimizing resources, but it doesn't have an automatic pause feature.
• Notebooks: Both Synapse Spark Pool and Azure Databricks offer notebook functionality. Synapse Spark Pool comes with nteract notebooks, while Azure Databricks uses Databricks notebooks.
• Library Management: Synapse Spark Pool allows you to manage libraries at the workspace, pool, or session level. Azure Databricks also supports library management, but it's more focused on workspace and cluster levels.
• Security: Both platforms provide security features, but Synapse Spark Pool has additional considerations for data access and networking when using a managed virtual network with Azure Synapse.
• CI/CD: Since you're already using Azure DevOps, Synapse Spark Pool and Azure Databricks can be integrated with Azure DevOps for CI/CD purposes.'=
• Ease of use: Synapse is an Easy-to-use interface suitable for users familiar with SQL and data analysis. Databricks use a lot of open-source ML libraries and require familiarity with Apache tools. Databricks is geared towards a more technical audience with experience managing clusters and configuration updates.

A similar thread has been discussed here

<Copied from the above MVP answer>

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

Here is a blog explained about the difference between Synpase and databricks:

Reference documents:

https://github.com/MicrosoftDocs/azure-docs/blob/main/articles/synapse-analytics/spark/apache-spark-overview.md

https://learn.microsoft.com/en-us/azure/databricks/
https://learn.microsoft.com/en-us/azure/databricks/dev-tools/ci-cd/ci-cd-azure-devops

https://learn.microsoft.com/en-us/azure/synapse-analytics/cicd/continuous-integration-delivery

Ultimately, the choice between Synapse Spark Pool and Azure Databricks depends on your specific requirements and preferences.

Suppose you prefer a unified analytical platform with seamless integration with other Synapse services, such as SQL serverless and dedicated SQL pools. In that case, Synapse Spark Pool might be a better choice. Also, if you want to leverage features like auto-pause for cost optimization, Synapse is preferred.

If you're looking for a standalone service with a strong focus on data engineering and data science collaboration for large-scale data processing and machine learning purposes, Azure Databricks could be more suitable.

I hope this helps. In case if you have any further questions, please let me know.

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