🌊 Delves into techniques to supercharge your data lake queries, from leveraging caching and in-memory data processing to utilizing advanced query engines like StarRocks. Learn how to achieve near-data warehouse speeds with lakehouse flexibility and discover the transformative potential of materialized views for on-demand query acceleration. Explore the details and see how fast your data engine can go! ---------------------------------------------------------------------------------------------------------------------- 00:00 How to Accelerate Data Lake Queries 00:09 Accelerating Raw Query Performance To accelerate data lakehouse queries, it's essential to focus on raw query performance by implementing caching to reduce I/O costs and improve stability. Query engines should leverage Massively Parallel Processing (MPP) and in-memory data shuffling to avoid the slower disk-based operations that are common in older systems like Spark. Optimizing for in-memory operations helps achieve lower latency and higher concurrency, significantly speeding up query response times. Additionally, using C++ for query engine development can exploit SIMD instruction sets, optimizing batch operations and enhancing overall performance. 03:50 How Fast Can A Lakehouse Engine Go? The performance of a lakehouse engine can be impressive, as demonstrated by StarRocks, which operates both as a data warehouse and a lakehouse engine. StarRocks excels in handling diverse workloads, supporting real-time analytics, and seamlessly switching storage engines to adapt to different data formats like Parquet or Apache Iceberg. Benchmarks such as the SSB show that StarRocks significantly outperforms competitors like ClickHouse and Apache Druid, especially in complex multi-table queries, illustrating its capability as a high-performance engine for both traditional data warehouses and modern lakehouses. 06:34 Benchmarking: StarRocks as a Data Warehouse vs StarRocks as a Lakehouse Query Engine In a comparative benchmark using the TPCD with a one-terabyte dataset, StarRocks as a data warehouse is only marginally faster than its lakehouse configuration—by about 12%. This minimal performance drop indicates that users can enjoy the benefits of a unified data architecture without significant compromises in speed. Such benchmarks reveal that modern lakehouse engines can nearly match traditional data warehouses in performance, even under complex query loads involving extensive joins and high-cardinality aggregations. 08:31 Seamless Query Acceleration With Materialized View Materialized views offer a robust solution for enhancing query performance within lakehouse architectures. By storing the results of queries and allowing direct query rewrites to these pre-computed results, materialized views address the need for on-demand query acceleration without necessitating changes to SQL scripts. This approach not only simplifies the query process for end-users but also reduces the computational and storage overhead associated with traditional pre-computation pipelines, thus accelerating the transition from development to production environments. 🎥 This video is part of our "5 Brilliant Lakehouse Architectures from Tencent, WeChat, and More" session. To watch in full, visit: https://www.youtube.com/watch?v=2Hhrn2jPSRk ----------------------------------------------------------------------------------------------------------------------- Learn more at https://celerdata.com/ Connect with us: LinkedIn: https://www.linkedin.com/company/celerdata/ Twitter: https://twitter.com/celerdata StarRocks GitHub: https://github.com/StarRocks/StarRocks StarRocks Website: https://www.starrocks.io/ Slack: https://try.starrocks.com/join-starrocks-on-slack #DataAnalytics #DataEngineering #DataLakeAnalytics #OLAP #DataAnalyst #DataEngineer #DataInfrastructure #Database #AnalyticalDatabase #DataLake #DataLakeHouse #DataWarehouse #DataScience #ApacheIceberg