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Table Of Contents
Engineering Lakehouses with Open Table Formats
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In this chapter, we explored the critical performance optimization techniques required for efficient data processing in modern lakehouse architectures. We began by examining storage optimization strategies, including partitioning, compaction, clustering, and cleaning, which minimize data scanning and I/O costs. Apache Iceberg, Hudi, and Delta Lake leverage these techniques differently, providing flexibility and scalability for varied analytical workloads.
We then delved into advanced query optimization methods, highlighting the role of column statistics, Bloom filters, and vectorized execution in enhancing query performance. These approaches effectively prune data and utilize intelligent indexing strategies to minimize query latency and maximize resource efficiency. Additionally, we discussed CBO, intelligent caching architectures, and materialized view optimization, demonstrating their impact on high-performance analytics in distributed data ecosystems.
Through practical...