Artificial intelligence
Azure Synapse Analytics (formerly SQL Data Warehouse) is Microsoft’s warehousing and analytics platform. It combines SQL Pools (provisioned warehouses), SQL Serverless (on-demand queries) and Spark Pools for big data analysis. Databricks provides a Lakehouse environment based on Spark, adapted to data engineering, AI and real-time processing. Kanerika’s comparative study highlights ten key differences between Synapse and Databricks.
Main focus: Synapse is designed for structured data warehousing and reporting; Databricks focuses on data engineering, data science and ML.
Processing engine: Synapse uses SQL engines (provisioned or serverless pools) and offers an integrated Spark engine; Databricks relies exclusively on Spark, optimized for large-scale workloads and real-time analytics.
Cloud integration: Synapse integrates seamlessly with Power BI, Azure Data Lake and Azure Machine Learning; Databricks supports Azure but can also run on AWS and GCP.
Machine learning: Synapse relies on Azure Machine Learning for model creation; Databricks offers MLflow, AutoML and a collaborative notebook environment for advanced ML.
Data management: Synapse is optimized for structured data (CSV, Parquet, JSON); Databricks manages structured, semi-structured and unstructured data via Delta Lake, with ACID transactions.
Real-time analytics: Synapse includes Azure Stream Analytics but focuses on batch analysis; Databricks excels in real-time streaming thanks to Structured Streaming.
Collaboration: Synapse Studio focuses on SQL; Databricks offers multi-language notebooks and real-time collaboration.
Security and governance: Synapse provides column-level security, dynamic masking and Azure AD integration; Databricks offers role-based access control and GDPR/HIPAA compliance via Unity Catalog.
Pricing model: Both use a pay-as-you-go model; Synapse charges for storage and computation separately; Databricks charges for DBUs with auto-scaling.
Use cases: Synapse is suitable for BI projects and traditional data warehouses; Databricks is ideal for heavy ETL pipelines, ML and real-time analytics.
For companies requiring robust warehouses, complex SQL queries and tight integration with Power BI, Synapse is the logical choice. It’s easy to administer and provides a familiar environment for SQL teams. For those developing data-intensive pipelines, using different types of data and building AI models, Databricks is the right platform. Many organizations use Synapse for the reporting layer and Databricks for preparation and AI.
Are Synapse and Databricks targeting the same use cases? No. Synapse is for structured warehousing and BI workloads; Databricks is for transformation, real-time analytics and ML.
Can you run Spark in Synapse? Yes, Synapse includes a Spark pool, but it’s less optimized and less flexible than Databricks’ full Spark environment.
What about streaming? Databricks excels with Structured Streaming; Synapse favors batch analysis and offers Azure Stream Analytics as a complement.
Which tool is the most open? Databricks can run on multiple clouds and handles a variety of data formats. Synapse is confined to Azure, but offers deep integration with Power BI and the rest of the Microsoft suite.