Category: Database, Data, Docker, github, artificial-intelligence
Companies can use real-time data warehouses to implement real-time Online Analytical Processing (OLAP) analytics, real-time data panels, real-time application monitoring, and real-time data interface services.
Thanks to Flink 1.11's enhanced support for the SQL language and TiDB's HTAP capabilities, we've combined Flink and TiDB to build an efficient, easy-to-use, real-time data warehouse that features horizontal scalability and high availability. In this article, I'll describe what a real-time data warehouse is, the Flink + TiDB real-time data warehouse's architecture and advantages, this solution's real-world case studies, and a testing environment with Docker Compose.
If you are interested in the Flink + TiDB real-time data warehouse or have any questions, you're welcome to join our community on Slack and send us your feedback.
Thanks to Flink 1.11's enhanced support for the SQL language and TiDB's HTAP capabilities, we've combined Flink and TiDB to build an efficient, easy-to-use, real-time data warehouse that features horizontal scalability and high availability. In this article, I'll describe what a real-time data warehouse is, the Flink + TiDB real-time data warehouse's architecture and advantages, this solution's real-world case studies, and a testing environment with Docker Compose.
If you are interested in the Flink + TiDB real-time data warehouse or have any questions, you're welcome to join our community on Slack and send us your feedback.
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