Knowing Your Data Partitioning Vices on the Data Lakehouse
Blog post from Onehouse
Data partitioning is a crucial strategy for enhancing query performance, scalability, and manageability, yet it requires careful design to avoid adverse effects. The blog explores how different databases and data warehousing systems, like Vertica, Teradata, Redshift, BigQuery, and Snowflake, implement partitioning to distribute data, improve performance, and manage availability. It highlights the evolution of data partitioning on data lakes, emphasizing the shift from row-based to column-based file formats like Apache Parquet and Apache Orc, which support better query performance through horizontal and vertical partitioning. The text discusses common pitfalls in data partitioning, such as mismatched partitioning schemes with query shapes, overly granular partitioning leading to small file problems, and inconsistent partitioning practices that can degrade query performance. The blog stresses the importance of choosing appropriate partition granularity and maintaining consistency across datasets, suggesting that modern data storage technologies offer opportunities to refine traditional partitioning approaches to ensure predictable performance and cost efficiency.
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