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Databases & messaging · #duckdb
Learn how DuckDB can query Parquet files directly, pushing down predicates and pruning columns for fast analytics without ETL.
Learn how DuckDB's ATTACH command lets you query multiple .duckdb files as if they were schemas, with a step‑by‑step example, limits, and common mistakes to avoid.
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.
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.
Learn how DuckDB pushes WHERE predicates and column projections into Parquet scans, reducing I/O without any ETL step.
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.
In a data ingestion pipeline, I need to insert rows into a DuckDB table using a simple INSERT statement. Occasionally, the operation fails due to a transient issue such as a locked file or a network glitch, and I want to retry the INSERT automatically. However, if the first attempt succeeded but the failure occurred after the commit, a naive retry would inse