- SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that leverages Massively Parallel Processing (MPP) to quickly run complex queries across petabytes of data.
- Import big data into SQL Data Warehouse with simple PolyBase T-SQL queries, and then use the power of MPP to run high-performance analytics.
- SQL Data Warehouse stores data into relational tables with columnar storage.
- This format significantly reduces the data storage costs, and improves query performance.
- Compared to traditional database systems, analysis queries finish in seconds instead of minutes, or hours instead of days.
- To save costs, you can pause and resume compute resources on-demand.
- For example, if you won't be using the database during the night and on weekends, you can pause it during those times, and resume it during the day.
- You won't be charged for compute resources while the database is paused. However, you will continue to be charged for storage.
- You can increase or decrease compute resources by adjusting data warehouse units.
- SQL Data Warehouse uses a node-based architecture.
- Applications connect and issue T-SQL commands to a Control node, which is the single point of entry for the data warehouse.
- The Control node runs the MPP 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.
- With decoupled storage and compute, SQL Data Warehouse can:
- Independently size compute power irrespective of your storage needs.
- Grow or shrink compute power without moving data.
- Pause compute capacity while leaving data intact, so you only pay for storage.
- Resume compute capacity during operational hours.
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