Azure Synapse Analytics: What It Actually Is and When It Makes Sense

Last updated: May 2025

What Synapse Actually Is

Microsoft positions Synapse as a "unified analytics platform," and that's roughly accurate -- it's several distinct services stitched together under one Azure portal experience. The key word is "unified." Before Synapse, you'd provision an Azure SQL Data Warehouse separately, set up Azure Data Factory for ETL, spin up HDInsight or Databricks for Spark, and wire them together yourself. Synapse puts all of those pieces into a single workspace with shared security, monitoring, and a common development environment called Synapse Studio.

The workspace gives you one place to write SQL queries, build Spark notebooks, create data pipelines, and manage access control. Under the hood, though, each component still runs on separate compute -- your dedicated SQL pool doesn't share resources with your Spark pool. That distinction matters for cost and performance, as we'll cover below.

The Five Core Components

1. Dedicated SQL Pool (formerly SQL DW)

This is Microsoft's MPP (massively parallel processing) data warehouse. You provision capacity in Data Warehouse Units (DWUs), which bundle compute, memory, and IO. It's T-SQL compatible, so your existing SQL Server skills transfer. You get columnar storage, hash-distributed and replicated table types, and result-set caching.

Pricing ranges from DW100c (~$1.20/hr) to DW30000c (~$360/hr). You can pause the pool to stop compute charges, but here's the gotcha: storage charges continue even when paused. If you're sitting on 10TB of data, you're still paying roughly $230/month in storage whether the pool is running or not.

2. Serverless SQL Pool

No infrastructure to provision. You point it at files in your Data Lake (Parquet, CSV, JSON, Delta) and query them with T-SQL using OPENROWSET or external tables. Pricing is per-TB-scanned -- about $5 per TB as of early 2026.

This is excellent for ad-hoc exploration: your analyst wants to peek at yesterday's log files, runs a query, pays a few cents. But watch out -- if someone writes a SELECT * against a 500GB Parquet folder and runs it 20 times while debugging, that's $50 in query costs for what feels like casual exploration. There's no pre-provisioned ceiling, so set cost controls from day one.

3. Apache Spark Pool

Managed Spark clusters for big data processing, machine learning, and notebook-based development. You define a node size and an autoscale range (minimum to maximum nodes). Spark pools support Python, Scala, .NET for Spark, and SparkSQL.

The cost trap here: Spark pools have a minimum node count (typically 3 nodes), and you're billed from the moment the pool starts until it auto-pauses after an idle timeout (default 15 minutes). A Medium-sized pool with 3 nodes runs about $2.40/hr. If your team leaves a pool running through an 8-hour workday, that's $19.20/day -- or ~$400/month -- even if it was only actively processing data for 2 hours.

4. Synapse Pipelines

These are functionally identical to Azure Data Factory pipelines. Same visual designer, same Copy Activity, same Mapping Data Flows, same connectors. The difference is that Synapse Pipelines live inside your Synapse workspace, so you can trigger Spark notebooks and SQL scripts directly without leaving the environment. If you're already familiar with building pipelines in ADF, you'll feel right at home. For a detailed comparison, check our ADF vs Synapse Pipelines guide.

Pricing is the same as ADF: per-activity execution, DIU-hours for Copy, and per-vCore-hour for Data Flows. One detail teams miss -- if you're already running standalone ADF for non-Synapse workloads, running Synapse Pipelines too means paying for two separate pipeline services.

5. Synapse Link

This is the most underrated piece. Synapse Link creates a live, no-ETL connection from operational databases -- Cosmos DB, Dataverse, and SQL Server -- into your Synapse workspace. For Cosmos DB specifically, it mirrors the analytical store (column-oriented copy of your transactional data) into Synapse with near-zero latency.

If you're running Cosmos DB and need to run analytical queries without impacting transactional performance, Synapse Link eliminates the need to build and maintain a separate ETL pipeline. That's a genuine win. The catch: Synapse Link for SQL Server is still in preview for some configurations, and Dataverse Link has specific licensing requirements through Power Platform.

When Synapse Makes Sense

All-Microsoft environment. Your data lives in Azure Data Lake Storage, your apps run on Azure, your team knows T-SQL, and your BI layer is Power BI. Synapse integrates natively with all of these. The Synapse-Power BI integration lets you connect Power BI datasets directly to Synapse pools without any data movement.

You need SQL + Spark in one place. If your data engineers write Spark notebooks for heavy transformations and your analysts query results with SQL, Synapse gives both teams a shared workspace with shared security. No separate Databricks workspace to manage.

Cosmos DB analytics. Synapse Link for Cosmos DB is genuinely one of the easiest ways to run analytical queries on transactional data. If you're already on Cosmos DB, this alone might justify Synapse.

Exploratory analytics on a data lake. The serverless SQL pool is one of the cheapest ways to query Parquet, Delta, and CSV files ad-hoc without provisioning anything. Great for data discovery phases.

When Synapse Doesn't Make Sense

You only need a data warehouse. If your requirement is "load data, run queries, serve dashboards," Snowflake and Amazon Redshift are simpler to operate. Snowflake's auto-scaling and separation of storage/compute are more mature. Redshift Serverless eliminates cluster management entirely. Synapse's dedicated SQL pool requires manual DWU scaling and has more operational overhead.

You only need ETL/ELT. Standalone Azure Data Factory does the same thing as Synapse Pipelines at the same price, without the overhead of a Synapse workspace. If your pipelines move data between storage accounts, databases, and SaaS applications and you don't need Spark or SQL pools, ADF is the right choice. See our guide on ADF incremental loads for common patterns.

Your team isn't on Azure. Synapse is deeply tied to the Azure ecosystem. If your data is in AWS S3 or GCP, you can technically connect to it, but you'll fight the tooling at every step. Multi-cloud architectures are better served by Snowflake or Databricks, both of which run natively across clouds.

You need fine-grained Spark control. Databricks offers better Spark performance tuning, a richer notebook experience, MLflow integration, and Unity Catalog for governance. If Spark is your primary workload, Databricks is usually the better choice -- Synapse Spark pools are adequate but not best-in-class.

Cost Gotchas to Watch

Synapse vs the Alternatives

Getting Started: A Practical Path

If you're evaluating Synapse, here's a low-risk way to start:

1. Create a Synapse workspace connected to an existing ADLS Gen2 account. This is free -- you only pay when you use compute.
2. Use the serverless SQL pool to query existing data lake files. This costs pennies per query and requires zero provisioning.
3. Build 2-3 pipelines using Synapse Pipelines to test your ETL patterns. Compare the experience to standalone ADF.
4. Only provision a dedicated SQL pool if you've confirmed that serverless SQL doesn't meet your performance needs for production dashboards. Start at DW100c and scale up.
5. Only add Spark pools if you have genuine big data processing or ML requirements that SQL can't handle.

Key Takeaways

• Synapse is a platform, not a product. It bundles 5 distinct services. You'll rarely use all of them, and each has its own pricing model.
• Dedicated SQL pool is a real MPP warehouse with T-SQL compatibility. It's powerful but requires manual scaling and charges for storage even when paused.
• Serverless SQL pool is the easiest entry point. Zero provisioning, pay-per-query, great for data lake exploration. Just set cost limits.
• Synapse Pipelines = ADF inside Synapse. Same engine, same price, but integrated with Spark and SQL pools.
• Synapse Link for Cosmos DB is a genuine differentiator. If you're on Cosmos DB, this is one of the strongest reasons to adopt Synapse.
• Don't adopt Synapse just because it's "unified." If you only need one piece (warehouse, ETL, or Spark), a focused tool is usually cheaper and simpler.

Frequently Asked Questions