Dedicated SQL Pool in Azure Synapse Analytics
🔹 What is a Dedicated SQL Pool?
A Dedicated SQL Pool in Azure Synapse Analytics (formerly Azure SQL Data Warehouse) is a scalable, high-performance data warehousing solution built on a Massively Parallel Processing (MPP) architecture. It enables analytics over large volumes of structured data using standard SQL.
🔹 Key Characteristics
- Formerly: Azure SQL Data Warehouse
- Deployment: Standalone or via Synapse Studio
- Architecture: MPP (Massively Parallel Processing)
- Elasticity: Compute and storage scale independently
- Cost Efficiency: Pause/resume compute resources to save costs
🔹 Core Components
- Control Node: Acts as the SQL endpoint and orchestrates query execution.
- Compute Nodes: Run the distributed portions of the query.
- Distributions: Data is distributed across 60 partitions for parallelism.
- Data Movement Service (DMS): Transfers data between nodes when needed.
⚙️ Query Execution Flow
- User submits query to the Control Node.
- The query is parsed and optimized, and an execution plan is created.
- Tasks are dispatched to Compute Nodes by the MPP engine.
- Each node processes its data slice independently.
- DMS transfers data as needed (for joins/aggregations).
- Final result is assembled by the Control Node and returned to the user.
🔹 Distribution Methods
- HASH: Distributes rows by hashing a column. Best for large fact tables.
- ROUND ROBIN: Evenly distributes rows randomly. Good for staging data.
- REPLICATE: Copies the table to all nodes. Ideal for small dimension tables.
Use the DISTRIBUTION clause to define the method during table creation.
⚙️ Data Warehouse Unit (DWU)
DWU (Data Warehouse Unit) represents the combined CPU, memory, and IO power provisioned for the Dedicated SQL Pool. It defines performance tiers.
| Performance Level | Compute Nodes | Memory (GB) |
|---|---|---|
| DW100c | 1 | 60 |
| DW200c | 1 | 120 |
| DW500c | 1 | 300 |
| DW1000c | 2 | 600 |
| DW1500c | 3 | 900 |
| ... | ... | ... |
| DW30000c | 60 | 18000 |
🧠SQL Features Support
Supported Features:
- Tables, Views, Indexes, Schemas
- Stored Procedures, Functions, Temp Tables
- Dynamic SQL, DML, DDL
- Dynamic Management Views (DMVs)
Not Supported:
- Triggers
- Cross-database queries
Works Differently Than SQL Server: Identity columns, constraints, and relationships behave differently than in traditional SQL Server environments.
✅ Benefits
- Massive scalability up to petabytes
- Cost-effective with pause/resume capabilities
- Ideal for enterprise-scale analytics and BI
- Supports full T-SQL language features
📺 Watch the Video Tutorial
📚 Credit: Content created with the help of ChatGPT and Gemini.
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