Querying Parquet Files Directly in DuckDB with Predicate Pushdown and Column Pruning
Learn how DuckDB can query Parquet files directly, pushing down predicates and pruning columns for fast analytics without ETL.
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Leveraging DuckDB's Automatic Column Pruning to Speed Up Wide Parquet Queries
Learn how DuckDB automatically prunes unneeded columns from Parquet files, see a concrete query example, verify the plan with EXPLAIN, and understand the limits of this optimization.
Zero‑Copy Parquet in DuckDB: Boost Data‑Science Performance without the Memory Cost
DuckDB’s zero‑copy Parquet reader lets you query terabyte‑scale datasets in Python with minimal memory, using column pruning and vectorised execution. This post walks through a practical example, shows how to verify the benefit, and discusses trade‑offs.
duckdbQuerying Parquet Files Directly in DuckDB with Predicate and Projection Pushdown
Learn how DuckDB pushes WHERE predicates and column projections into Parquet scans, reducing I/O without any ETL step.
Choosing Between Parquet and Avro for Spark ETL Pipelines
A technical decision guide for choosing between Parquet and Avro in Apache Spark, comparing read/write performance, schema evolution, and columnar vs. row-based storage.
Zero‑Copy Analytics: Querying Remote Parquet Files Directly with DuckDB
Discover how DuckDB lets you query remote Parquet files directly, reducing data egress and speeding up analytics with zero‑copy, predicate pushdown, and an in‑process columnar engine.