Azure Synapse vs. Databricks: A comparison

Azure Synapse vs. Databricks: Ein Vergleich

In the world of data analysis and big data management, Azure Synapse Analytics and Databricks are two prominent names. Both offer powerful tools for processing and analyzing large volumes of data, but differ in their core functions and areas of application. In this article, we take a look at the differences between Azure Synapse and Databricks.

Azure Synapse Analytics

Azure Synapse is an analytics service that combines data integration, enterprise data warehousing and big data analysis.

Key functions:

  • Data warehousing: Synapse is primarily a data warehouse tool that enables the storage and analysis of large amounts of data in relational databases.
  • Integration of data pipelines: It provides tools to easily create data pipelines for data movement and transformation.
  • SQL and Spark support: Synapse enables the processing of data with both SQL and Apache Spark.
  • Integrated BI tools: It offers tight integrations with Power BI and Azure Machine Learning.

Databricks

Databricks is a platform for big data analytics and machine learning supported by Apache Spark.

Key functions:

  • Apache Spark-based: Databricks is a Spark-based platform known for its ability to process data quickly.
  • Machine learning and AI: It offers advanced functions for machine learning and AI applications.
  • Data Lake Integration: Databricks works well with data lakes, especially with Azure Data Lake Storage.
  • Collaborative work environment: It fosters a collaborative environment for data scientists and engineers.

Differences

Target group and use case:

  • Synapse: Ideal for companies that need a powerful data warehouse combined with the ability to integrate data and business intelligence.
  • Databricks: Best suited for scenarios that require powerful data processing and advanced analytics, especially in the field of machine learning and AI.

Performance and scalability:

  • Synapse offers optimized performance for data warehousing and SQL-based queries.
  • Databricks excels at processing large volumes of data and complex analytical workloads in real time.

Ease of use:

  • Synapse offers deeper integration with other Azure services, making it a natural choice for existing Azure customers.
  • Databricks offers a more user-friendly interface for data science teams and better support for Spark.

Connection of Azure Synapse to Databricks

The integration of Azure Synapse Analytics and Databricks offers a powerful combination for data processing and analysis. It is quite possible and often advisable to combine both services in order to make optimum use of the strengths of each tool.

Integration techniques:

  • Data sharing: Data can be exchanged between Synapse and Databricks by storing it in a shared data lake to which both have access.
  • Direct connection: Databricks can directly access Synapse SQL pools to read or write data using JDBC/ODBC drivers.
  • Using Azure Data Factory: Azure Data Factory can serve as a bridge to create data pipelines that transfer data between Synapse and Databricks.

Final review

The choice between Azure Synapse and Databricks depends heavily on a company’s specific needs and goals. While Synapse is suitable for traditional data warehousing and business intelligence tasks, Databricks is the better choice for complex data processing and machine learning. However, both tools complement each other and can work together effectively in a comprehensive data strategy.

You can also dock Synapse to Databricks and thus combine both tools.

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