What Synapse does, its SQL pools, Spark pools and pipelines, the 4-layer architecture, how pricing works, when to use it — and how it relates to Microsoft Fabric.
✍️ Azure Trainings trainers⏱ 4 min read📅 Updated 11 October 2026
Azure Synapse Analytics brings data warehousing, big data processing and data integration into one workspace. You load data with Synapse Pipelines, query it with dedicated or serverless SQL pools or Spark pools, and report on it in Power BI. Microsoft now offers Microsoft Fabric as its newer all-in-one platform, but Synapse is still supported and runs many existing data platforms.
Synapse was earlier called Azure SQL Data Warehouse. It works closely with ADLS Gen2, Power BI and Azure Machine Learning. Want to learn this hands-on? Join our Azure Data Engineer Training in Hyderabad.
Building blocks
Core components of Synapse
🏛️
Dedicated SQL pool
A provisioned warehouse using massively parallel processing (MPP)
Best for steady, predictable reporting
Can be paused to stop compute charges
☁️
Serverless SQL pool
Query CSV, Parquet and JSON in the lake with T-SQL
Nothing to set up, no charge when idle
Best for ad-hoc analysis and exploration
⚡
Apache Spark pool
Managed Spark for data engineering and ML
Auto-scaling pools
🔄
Synapse Pipelines
Drag-and-drop ETL/ELT — same engine as Azure Data Factory
Schedule or event triggers
⚡ Short answer
The Data Explorer pool (preview) was retired by Microsoft on 7 October 2025. Microsoft recommends Eventhouse in Microsoft Fabric instead.
How it works
Azure Synapse architecture: 4 layers
Layer 1Pipelines / ADF
Ingestion
Layer 2ADLS Gen2
Storage
Layer 3SQL & Spark pools
Compute
Layer 4Power BI, Azure ML
Consumption
Features
Key features you’ll use most
🧭
One workspace
Warehousing, Spark, pipelines and reporting in one place.
🖥️
Synapse Studio
Browser workspace for SQL scripts, notebooks, pipelines and monitoring.
📊
Power BI integration
Reports on Synapse data without copying it.
🔐
Security
Encryption, RBAC, managed identities, private endpoints; Purview for governance.
📏
Separate scaling
Scale compute without changing storage.
💸
Cost control
Pay per query on serverless; pause dedicated pools.
🧭 Want hands-on practice with SQL pools and Spark pools? Our trainers guide you in live Synapse labs.
Combine ERP, CRM and web app data into one trusted source.
🤖
Analytics & ML
Explore data and train models with Spark and Azure ML.
📊
Business intelligence
Power BI dashboards; serverless SQL to query files in place.
⚡
Near real-time data
Spark Structured Streaming on Event Hubs, IoT Hub or Kafka data.
Costs
How Azure Synapse pricing works
↔️ Swipe the table sideways
ComponentHow you payServerless SQL poolPer TB of data processed by your queriesDedicated SQL poolPer hour for provisioned capacity (DWUs); pause to stop compute chargesSpark poolsPer vCore-hour while the pool runsPipelinesPer activity run and data movementStorageADLS Gen2 storage billed separately
✕ Leaving a dedicated SQL pool running 24/7 when it isn’t needed
✕ Running huge scans on serverless SQL without partitioned Parquet
✕ Building new projects on the retired Data Explorer pool
Learn Synapse and Fabric hands-on Our Azure Data Engineer training in Hyderabad covers Synapse SQL and Spark pools, pipelines and Microsoft Fabric with real projects.
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