Why Database Columnar Storage Crushes Analytic Queries

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Why Database Columnar Storage Crushes Analytic Queries

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Lucas and Luna dive into why database columnar storage formats like Parquet and ORC have become essential for analytic workloads, especially in cloud environments. They explore how columnar storage differs from row-based storage, using concrete examples from Amazon Redshift and Google BigQuery. The episode walks through compression benefits, predicate pushdown, and how choosing the right format can reduce query times from minutes to seconds. Luna challenges the notion that columnar storage is always superior, and Lucas explains when row-based still wins. They also touch on how DuckDB and ClickHouse are pushing columnar storage further, and what this means for developers building data pipelines in mid-2026. #ColumnarStorage #DatabaseTechnology #Parquet #ORC #AmazonRedshift #GoogleBigQuery #DuckDB #ClickHouse #AnalyticQueries #DataPipelines #QueryOptimization #Compression #PredicatePushdown #CloudComputing #BigData #DataEngineering #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

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