Azure Synapse vs Databricks

[zmsoft]

Hi there,

I would like to know the difference between Azure Databricks and Azure Synapse, which use case is Databricks appropriate and which use case is Synapse appropriate? What are the differences in their functions? What are the differences in their costs?

Thanks & Regards,

zmsoft

[agallard]

HI @zmsoft,

Although it is a very generic and complicated question to answer without knowing more about the data solution you need, I will leave you with some characteristics of both services. As always, the final decision you make will depend on the needs of the project.

Azure Databricks and Azure Synapse Analytics are both powerful data processing tools on Azure, but they have distinct purposes, strengths, and cost structures. Here’s a comprehensive comparison to help you understand the appropriate use cases for each and their functional differences.

1. Overview of Azure Databricks and Azure Synapse Analytics

• Azure Databricks:

  • A unified data and analytics platform that combines the capabilities of Apache Spark with data lake integration, machine learning, and collaborative data engineering workflows.
  • Provides a notebook-based development environment with extensive support for Spark, Delta Lake, and machine learning libraries.

• Azure Synapse Analytics:

  • A comprehensive analytics service that unifies big data, data integration, and data warehousing. Synapse combines SQL-based data warehouse capabilities with Spark, Pipelines for ETL, and Synapse Studio for management.
  • Offers both on-demand (serverless) and provisioned (dedicated) compute options for flexible data processing.

2. Core Use Cases

Use Case Azure Databricks Azure Synapse Analytics

Big Data Processing	High-performance data processing with Spark and Delta Lake, especially for unstructured and semi-structured data.	Best for structured data and big data transformations; supports Spark but often less customizable than Databricks for Spark jobs.
Machine Learning	Robust for data science, ML, and advanced analytics with libraries like MLlib, TensorFlow, and scikit-learn.	Limited ML capabilities; best for SQL-based analytics and data warehousing but integrates with Azure Machine Learning.
ETL/ELT Workflows	Strong ETL capabilities; ideal for real-time transformations and data engineering with Delta Lake.	Synapse Pipelines enable orchestrated ETL jobs across various data services (SQL, Spark, and external connectors).
Data Lake Exploration	Efficient for reading, transforming, and writing large-scale data lakes. Ideal for Lakehouse architectures with Delta Lake.	Good for data lake exploration, but best suited for structured data and SQL-based transformations in a warehousing context.
Data Warehousing	Not designed specifically as a data warehouse but can be adapted with Delta Lake.	Primary function as a data warehouse, supporting massive structured data storage with SQL-based analytics.

3. Functional Differences

Feature Azure Databricks Azure Synapse Analytics

Primary Language Support	Python, Scala, SQL, R (focused on Spark-based development)	SQL (T-SQL), Spark (less customizable than Databricks), and Data Explorer
Data Format Support	Optimized for Delta Lake, Parquet, CSV, JSON, AVRO	Optimized for SQL tables, Parquet, and Delta Lake with some support for CSV, JSON
Collaboration	Real-time collaborative notebooks, integrated Git support	Less interactive for real-time collaboration; Synapse Studio enables SQL-based collaboration
Compute Management	Autoscaling clusters, serverless SQL pools and serverless available.	Provisioned and on-demand (serverless) SQL pools for flexible compute; Spark pools with limited customization
Security	Integrates with Azure Active Directory (AAD), supports Role-Based Access Control (RBAC), and Unity Catalog for data governance	Integrates with AAD, and RBAC; Azure Synapse Security features for SQL and Spark pools
Optimizations	Delta Lake optimizations (Z-Ordering, OPTIMIZE, etc.), autoscaling for Spark workloads	Optimizations for SQL pools, caching, partitioning; Spark optimizations are more limited compared to Databricks

4. Cost Structure

• Azure Databricks:

  • Compute Cost: Based on Databricks Units (DBUs), which represent processing time in terms of DBU/hour. Costs vary by VM type and workload (Standard, Premium, or Enterprise).
  • Serverless SQL Pools: Available as a cost-effective, on-demand option for SQL queries.
  • Autoscaling Clusters: Helps manage costs by scaling up and down based on workload needs.
  • Delta Lake Cost Efficiency: Efficient for large datasets due to Delta Lake optimizations (e.g., Z-ordering), which help minimize data scanning.

• Azure Synapse Analytics:

  • Dedicated SQL Pools: Billed based on reserved capacity (DWUs), ranging from small workloads to very large data warehouses.
  • Serverless SQL Pools: Pay-per-query model, making it cost-effective for exploratory or infrequent SQL queries.
  • Spark Pools: Separate from SQL pools; pricing is based on provisioned Spark nodes.
  • ETL Costs: Synapse Pipelines is based on Data Integration Units (DIUs) for ETL workloads, which is comparable to Azure Data Factory’s pricing.

5. Selecting the Right Tool for Specific Scenarios

• Choose Azure Databricks for:

  • Real-time and batch data transformations with Apache Spark.
  • Advanced machine learning and AI workloads with extensive library support.
  • Data lakehouse architecture needs, leveraging Delta Lake for reliability and performance.
  • Collaborative data engineering and analytics with interactive notebooks.

• Choose Azure Synapse Analytics for:

  • Traditional data warehousing and SQL-based analytics at scale.
  • Unified analytics with SQL, Spark, and integration capabilities in a single platform.
  • Cost-effective, serverless options for SQL-based exploration on large datasets.
  • Scenarios requiring tight integration with Azure Data Factory or SQL-based ETL workflows.

6. Example Comparison: Typical Workflows

• Data Engineering Workflow:

  • Azure Databricks: Ideal for ETL pipelines involving unstructured and semi-structured data, processing data with Spark and Delta Lake. Interactive exploration and machine learning model development are seamless.
  • Azure Synapse: Suitable for structured data ETL with Synapse Pipelines, typically transforming data stored in SQL tables or Synapse’s data lake. Best for SQL-based transformations.

• Data Science and Machine Learning Workflow:

  • Azure Databricks: Databricks shines in this scenario, providing support for data science libraries, distributed ML, and model training.
  • Azure Synapse: Limited support; while Spark pools exist, it’s not as robust as Databricks for machine learning workflows.

• Data Warehousing Workflow:

  • Azure Databricks: Delta Lake supports ACID transactions, making it feasible for some warehousing needs, but it’s more complex to configure as a traditional warehouse.
  • Azure Synapse: Primarily designed for warehousing with high-performance SQL and data storage, with optimizations for structured data.

Azure Databricks and Azure Synapse Analytics serve different purposes within the data analytics ecosystem on Azure.

Databricks is best for Spark-based data processing, machine learning, and real-time transformations, while Synapse is optimized for large-scale SQL data warehousing, integration, and SQL-based analytics.

Cost-effectiveness depends heavily on the workload: Databricks offers autoscaling and pay-per-use clusters, whereas Synapse provides a mix of serverless and provisioned compute options for SQL and Spark.

ℹ️If you ask me, I'll tell you Databricks😁

👉Let me know if you need more details on specific functionalities or examples to clarify!

Regards!

[FabianGutierrez]

Great Comparison list @agallard ! Do you also happen to have or know of a comparison list between Microsoft Fabric and Databricks? 

[agallard]

Hi @FabianGutierrez 

Not at the moment, but I will share it when I have it.

[VZLA]

I'm not sure about costs, but hope this helps with the other questions:

https://learn.microsoft.com/en-us/data-engineering/playbook/articles/databricks-vs-synapse

[NandiniN]

Hey @zmsoft ,

I was referring to some blogs, and on price part - 

Azure Synapse analytics is on a Pay-As-You-Go (PAYG) pricing model, allowing its users to only pay for what they use.

For Azure Databricks Pricing is also a PAYG model based on the total consumed Databricks Units (DBU). Customers can get discounts off the standard on-demand price by committing to certain usage periods.

Your account executives can guide you with the pricing better.

Also saw some other community members discussing on similar topic, if you want to join the conversation, please reply. https://community.databricks.com/t5/get-started-discussions/azure-synapse-vs-databricks/td-p/77122 

[thelogicplus]

share you use case i will suggest you about technology difference and which could be benefical for you. I love Data brick due to many awesome feature that help sql developer to programmer(python/Scala) to solve the use case on DataBricks. 

but if you want to migrate from one technology to Databrick then You can use Travinto Technologies code converter tool  to migrate data , ETL, and report from one technology to others. we have migrated Azure Synapse Analytics data to Databricks using their services without worry for many customer. They have 50000+ adaptor that can help you to migrate any thing to any things.