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Table Of Contents
Engineering Lakehouses with Open Table Formats
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At a fundamental level, storage optimization techniques ensure that data is structured and managed in a way that minimizes redundant operations and improves data locality. Techniques such as partitioning, compaction, and clustering have become integral to addressing the challenges of large-scale storage. Partitioning logically segments data based on specific columns, allowing query engines to target only relevant partitions. Without effective partitioning, systems risk scanning massive amounts of irrelevant data, which increases query latency and resource consumption. Compaction addresses a common issue in the big data world: the small file problem. It combines small data files, typically created during ingestion, to create larger ones, as querying large files is more efficient than processing many small files. This improves I/O performance, reduces the number of files read, and ultimately enhances overall query performance. Clustering, on the other hand,...