Azure Synapse Analytics - Unified Analytics Platform

Notice: Microsoft positions Microsoft Fabric as the long-term platform for many analytics scenarios and recommends Fabric for new data analytics projects. A complete retirement of Azure Synapse Analytics has not been announced, but individual components are being scaled back: Azure Synapse Data Explorer (preview) has already been retired (recommended migration: Fabric Eventhouse), and Azure Synapse Runtime 3.4 reaches end of life on March 31, 2026. Existing Synapse workloads, especially Dedicated SQL Pools, can continue to run, but Microsoft recommends planning a migration path to Fabric for Spark pools and pipelines.

Azure Synapse Analytics on Microsoft Azure

What is Azure Synapse Analytics?

Azure Synapse Analytics is Microsoft’s unified analytics platform, combining data warehousing (Dedicated SQL Pools), big data analytics (Apache Spark), data integration (pipelines), and serverless queries (Serverless SQL Pools) in one integrated environment. Synapse Studio provides a unified web IDE for SQL development, Spark notebooks, pipeline orchestration, and monitoring.

At its core, Synapse combines three compute engines: Dedicated SQL Pools for traditional data warehousing with massively parallel processing (MPP), Serverless SQL Pools for ad hoc queries over data lakes without provisioning, and Spark Pools for big data processing with Scala, Python, .NET Spark, and R. All engines can query the same data in Azure Data Lake Storage (ADLS Gen2), so you can choose the right engine per workload without duplicating data.

Data integration pipelines (based on Azure Data Factory) orchestrate ETL/ELT workflows that ingest data from many sources, transform it (via SQL, Spark, or data flows), and load it into Synapse pools or external systems. Power BI integrates natively for visualization, Azure Machine Learning for ML models on warehouse data, and Purview for data governance. Synapse is optimized for petabyte-scale analytics with automatic caching, columnstore indexes, and adaptive query optimization.

Typical Use Cases

Enterprise Data Warehouse Migration

Organizations migrate on-premises data warehouses (Teradata, Oracle, SQL Server) to Dedicated SQL Pools. Synapse offers an MPP architecture similar to Teradata, T-SQL compatibility, and auto-tuning.

Data Lake Analytics with Serverless SQL

Analysts query Parquet, CSV, and JSON files directly in a data lake using Serverless SQL Pool without loading data. Billing is based purely on the amount of data scanned, without provisioning a compute cluster. Ideal for ad hoc analysis of historical data without the ongoing cost of a dedicated pool.

Big Data Processing with Spark

Data engineers process streaming data, transform raw data, and train ML models with PySpark or Scala Spark. Spark Pools scale automatically and pause when idle.

Unified Analytics Platform

Teams use Synapse as a single platform for all analytics workloads: SQL analysts use dedicated pools for reports, data scientists use Spark notebooks for ML, and data engineers use pipelines for ETL. Everyone shares the same data lake, metadata is synchronized, and RBAC is unified.

Real-Time Analytics with Synapse Link

Synapse Link replicates operational data from Cosmos DB or SQL Database to Synapse in near real time without custom ETL. Analytical queries run against replicas without impacting production.

Best Practices for Azure Synapse Analytics

Choose the Right SQL Pool Option

Serverless for ad hoc queries, exploration, and BI tools with sporadic access (billed per TB scanned). Dedicated for predictable, continuous workloads and complex joins (billed per DWU-hour). Combine both: Serverless for data lake exploration, Dedicated for the core data warehouse.

Optimize Data Lake Structure

Use Parquet or Delta Lake format instead of CSV for columnar compression and faster queries. Partition by frequently used filter criteria (for example, date). Avoid small files (under 128 MB); use compaction. A well-designed structure can significantly reduce query costs.

Configure Spark Pool Auto-Pause

Enable auto-pause (for example, after 15 minutes idle) for dev and ad hoc workloads. Disable it for 24/7 streaming jobs (startup latency of 2-4 minutes). Reserved instances can reduce costs for continuous Spark workloads.

Implement Columnstore and Partitioning

Dedicated SQL Pools use columnstore indexes automatically for analytics workloads. Partition large tables (over 60 million rows) by date. Use distribution (hash, round-robin, replicated) for optimal query performance. Statistics update automatically.

Monitoring and Cost Management

Monitor query performance via DMVs and the Synapse Studio query monitor. Identify expensive queries and optimize them. Use workload management for resource isolation between user groups. Pause dedicated pools when not in use.

Frequently Asked Questions about Azure Synapse Analytics

What is the difference between Dedicated and Serverless SQL?

Dedicated SQL Pool is a provisioned data warehouse with fixed DWUs (Data Warehouse Units); you pay per hour regardless of usage. Serverless SQL Pool is on-demand with no provisioning, billed based on data scanned. Use Dedicated for continuous workloads, Serverless for ad hoc analysis and exploration.

Can I migrate an existing SQL Data Warehouse?

Yes, Azure SQL Data Warehouse was renamed to Synapse Dedicated SQL Pool. Existing SQL DW instances are automatically part of Synapse. Migration is transparent with no downtime. Extend with Serverless SQL and Spark Pools as needed.

How does Synapse Link work?

Synapse Link replicates data from Azure Cosmos DB, SQL Database, or Dataverse to Synapse in near real time without custom ETL. It provides automatic schema synchronization with no performance impact on the source. Query operational data with Serverless SQL or Spark without delays. Ideal for HTAP (hybrid transactional/analytical processing).

Which Spark versions are supported?

Synapse supports Apache Spark 3.x, which offers improved performance and Delta Lake support compared to earlier versions. Runtimes include PySpark, Scala Spark, .NET Spark, and R.

What does Azure Synapse Analytics cost?

Dedicated SQL Pools are billed per DWU-hour (Data Warehouse Units), Serverless SQL per TB of data scanned, Spark Pools per vCore-hour with an auto-pause option, and pipelines using Data Factory-like activity-based pricing. Current prices are listed on the official Azure pricing page.

How does Synapse integrate with Power BI?

Power BI connects directly to Synapse SQL Pools (Dedicated or Serverless) for live queries or import. Synapse Studio integrates Power BI for visualization without context switching. DirectQuery on Serverless SQL avoids data duplication. Materialized views improve BI performance.

Is Azure Synapse Analytics still suitable for new projects?

Microsoft positions Microsoft Fabric as the strategic platform for new analytics projects. For existing Synapse workloads, especially Dedicated SQL Pools, there is no announcement of a full retirement, but individual components (for example, older runtimes, Data Explorer) are being phased out. New projects should evaluate Fabric; existing workloads should plan a migration strategy.

Integration with innFactory

As a Microsoft Solutions Partner, innFactory supports you with data warehouse modernization and analytics platform implementation using Azure Synapse. We help with architecture, legacy DW migration, and performance optimization.

Contact us for a non-binding consultation on Azure Synapse Analytics and modern data warehousing.