📊 StarRocks Materialized View is a dynamic tool designed to optimize query performance by simplifying the ETL (Extract, Transform, Load) process. This feature acts like a standard database table, storing the results of queries in StarRocks' own format, and is capable of pre-computation. It can integrate data from both internal and external tables, including sources like Apache Hive, Apache Hudi, Iceberg, and Delta Lake.

Key features include:
🌟Versatility in Data Sources: Ability to build on a mix of internal and external tables.
🌟Support for Complex Queries: Handles both single and multi-table queries efficiently.
🌟Incremental and Flexible Refresh: Materialized views in StarRocks are updated incrementally by partition and can be refreshed manually, on a schedule, or triggered by data changes.
🌟Automatic Query Rewrite: StarRocks' cost-based optimizer can rewrite queries to utilize the most efficient materialized view, eliminating the need for SQL modifications.

Creating a materialized view in StarRocks involves simple SQL commands, with options to specify partitioning (essential for incremental refresh) and refresh policies like triggered, scheduled, or manual. Additionally, users can assign resource groups to safeguard mission-critical workloads.

By leveraging the Materialized View in StarRocks, users gain significant performance improvements without the need for altering their SQL queries, making it a powerful tool for efficient data management and analysis.

🎥 This video is part of our 'How to Boost Your Data Lake Performance with Materialized Views' webinar. To watch in full, visit:    • How to Boost Your Data Lake Performan...  
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