{"id":2142,"date":"2018-10-02T00:00:00","date_gmt":"2018-10-02T00:00:00","guid":{"rendered":"https:\/\/azure.microsoft.com\/blog\/enabling-real-time-data-warehousing-with-azure-sql-data-warehouse"},"modified":"2023-05-11T15:36:48","modified_gmt":"2023-05-11T22:36:48","slug":"enabling-real-time-data-warehousing-with-azure-sql-data-warehouse","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/enabling-real-time-data-warehousing-with-azure-sql-data-warehouse\/","title":{"rendered":"Enabling real-time data warehousing with Azure SQL Data Warehouse"},"content":{"rendered":"

Gaining insights rapidly from data is critical to competitiveness in today\u2019s business world. Azure SQL Data Warehouse<\/a> (SQL DW), Microsoft\u2019s fully managed analytics platform leverages Massively Parallel Processing (MPP) to run complex interactive SQL queries at every level of scale.<\/p>\n

Users today expect data within minutes, a departure from traditional analytics systems which used to operate on data latency of a single day or more. With the requirement for faster data, users need ways of moving data from source systems into their analytical stores in a simple, quick, and transparent fashion. In order to deliver on modern analytics strategies, it is necessary that users are acting on current information. This means that users must enable the continuous movement from enterprise data, from on-premise to cloud and everything in-between.<\/p>\n

SQL Data Warehouse is happy to announce that Striim now fully supports SQL Data Warehouse as a target for Striim for Azure. Striim enables continuous non-intrusive performant ingestion of all your enterprise data from a variety of sources in real time. This means that users can use intelligent pipelines for change data capture from sources such as Oracle Exadata straight into SQL Data Warehouse. Striim can also be used to move fast moving data landing in your data lake into SQL Data Warehouse with advanced functionality such as on-the-fly transformation and model based scoring with Azure Databricks.<\/p>\n

\"striim_Final_Logo_rgb_lightbg_header\"<\/p>\n

\n

“Enterprises adopting cloud-based analytics need to ensure reliable, real-time and continuous data delivery from on-prem and cloud-based data sources to reduce decision latencies inherent in batch based analytics. Striim’s solution for SQL Data Warehouse is offered in the Azure marketplace, and can help our customers quickly ingest, transform, and mask real time data from transactional systems or Kafka into SQL Data Warehouse to support both operational and analytics workloads”.<\/em>\u00a0<\/p>\n

\u2013 Alok Pareek, Founder and EVP of Products for Striim.<\/p>\n<\/blockquote>\n

Via in-line transformations, including denormalization, before delivering to Azure SQL Data Warehouse, Striim reduces on-premises ETL workload as well as data latency. Striim enables fast data loading to Azure SQL DW<\/a> through optimized interfaces such as streaming (JDBC) or batching (PolyBase). Azure customers can store the data in the right format, and provide full context for any downstream operations, such as reporting and analytical applications.<\/p>\n

\"Arch\"<\/p>\n

Next steps<\/h2>\n

To learn more about how you can build a modern data warehouse using Azure SQL Data Warehouse and Striim, watch this video<\/a>, schedule a demo<\/a> with a Striim technologist, or get started now on the Azure Marketplace<\/a>.<\/p>\n

Learn more about SQL DW and stay up-to-date with the latest news by following us on Twitter @AzureSQLDW<\/a>.<\/p><\/p>\n","protected":false},"excerpt":{"rendered":"

SQL Data Warehouse is happy to announce that Striim now fully supports SQL Data Warehouse as a target for Striim for Azure. 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