{"id":46240,"date":"2022-09-22T08:00:00","date_gmt":"2022-09-22T15:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/sql-server\/blog\/?p=46240"},"modified":"2024-04-19T10:27:56","modified_gmt":"2024-04-19T17:27:56","slug":"azure-synapse-link-for-sql","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/sql-server\/blog\/2022\/09\/22\/azure-synapse-link-for-sql\/","title":{"rendered":"Azure Synapse Link for SQL"},"content":{"rendered":"\n

Near-real-time analytics for transactional workloads<\/h2>\n\n\n\n

Part of the SQL Server 2022 blog series<\/a><\/em>.<\/p>\n\n\n\n

Traditionally, data to serve analytical systems have been extracted from operational data stores using custom-built extract, transform, and load (ETL) processes. These processes are often long-running, exert pressure on the source systems, and only run periodically in batch mode. While this kind of latency and overhead may be acceptable for some workloads, more and more companies are finding themselves in a place where they need to do analytics over operational data closer to real-time\u2014something that traditional ETL systems cannot support.<\/p>\n\n\n\n