Azure Synapse Analytics is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms.
Dedicated SQL pool (formerly SQL DW) uses a node-based architecture. Applications connect and issue T-SQL commands to a Control node. The Control node hosts the distributed query engine, which optimizes queries for parallel processing, and then passes operations to Compute nodes to do their work in parallel.
The Compute nodes store all user data in Azure Storage and run the parallel queries. The Data Movement Service (DMS) is a system-level internal service that moves data across the nodes as necessary to run queries in parallel and return accurate results.
Imagine your business data as a vast ocean, and analyzing it effectively requires a powerful fleet of ships and a well-organized port. Dedicated SQL pool in Azure Synapse Analytics acts as that fleet and port, efficiently processing your data through its key components:
1. Control Node
Think of it as the lighthouse, guiding your queries to the right ships. It receives your T-SQL commands and orchestrates the entire execution process.
2. Compute Nodes
These are the workhorses, like cargo ships, performing the actual data processing. Your data resides in Azure Storage (think warehouses on shore), and compute nodes retrieve and analyze it as instructed. Dedicated SQL pool allows scaling these nodes up or down based on your workload, just like adding or removing ships from your fleet.
3. Distributed Query Engine (DQP)
Picture this as the captain on the lighthouse, planning the most efficient route for your queries. It breaks down your queries into smaller tasks and distributes them among the compute nodes for parallel processing, like sending ships to different areas of the ocean for faster exploration.
4. Data Movement Service (DMS)
As data travels between ships and warehouses, this internal service ensures smooth and efficient transfer. It acts like the port’s cranes, loading and unloading data as needed.
5. Azure Storage
This vast storage space holds all your data, like warehouses storing cargo. Dedicated SQL pool utilizes Azure Blob Storage or Azure Data Lake Storage, offering flexibility and scalability for your information.
Example:
Imagine you want to analyze sales data across all your stores for the past year. Your query arrives at the control node, like a captain’s order. The DQP breaks it down into tasks, sending each compute node to analyze data from specific stores (ships exploring different regions). DMS coordinates data transfer, and the results are aggregated back to the control node, providing you with a complete picture of your sales performance (the final report delivered back to the lighthouse).
Remember, this is a simplified analogy, but hopefully, it gives you a clear understanding of how dedicated SQL pool’s architecture enables fast and efficient data analysis for even the largest datasets.
Process of designing a data warehouse with dedicated SQL pool (formerly SQL DW)
The following graphic shows the process of designing a data warehouse with dedicated SQL pool (formerly SQL DW):
