How to accelerate Apache Iceberg queries and fine-tune Iceberg for demanding analytics workloads? Discover tips and strategies from successful Apache Iceberg users within the StarRocks community.

00:00 Intro

00:27 Metadata Rewrite - Faster Planning, Better Data Pruning: Adjust manifest files to optimize query engine planning. Balancing the size of manifest files ensures efficient parallelization and speeds up the planning phase, enhancing overall query performance.

01:38 Data Compaction - Faster Data Scanning: After data ingestion, ensure proper data compaction to prevent small scattered files, which can hinder query efficiency. Compact data files enable faster scanning and reduce interactions with cloud object storage, minimizing costs and enhancing performance.

03:52 Utilize Iceberg Features: Explore and leverage Iceberg features like partitioning and sorting to simplify processes and improve query efficiency. Treat Iceberg as a data warehouse within a data lake, applying familiar optimization techniques for faster queries.

04:59 Choose the Right Query Engine: Select a query engine tailored to your workload requirements. For high-concurrency, low-latency workloads, consider StarRocks paired with Apache Iceberg, offering optimized performance for data warehouse-like queries.

05:56 What is StarRocks

07:04 Comparing StarRocks to Trino on Apache Iceberg

🎥 This video is part of our "Apache Iceberg + StarRocks: Your Recipe for Superior Lakehouse Performance" webinar. To watch in full, visit:    • Apache Iceberg + StarRocks: Your Reci...  
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