Using DuckDB’s Native Parquet Engine for Zero‑Config Analytics
Learn how DuckDB lets you read, transform, and write Parquet files directly from SQL without extra services or complex setup.
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Learn how DuckDB lets you read, transform, and write Parquet files directly from SQL without extra services or complex setup.
Learn how DuckDB can query Parquet files directly, pushing down predicates and pruning columns for fast analytics without ETL.
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.
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.
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.
Goal Determine whether DuckDB preserves TIMESTAMP WITH TIME ZONE and INTERVAL columns through a write-to-Parquet then read-from-Parquet cycle without changing the SQL type or losing values. Constraints DuckDB offers read_parquet() and COPY ... TO ... (FORMAT PARQUET), but INTERVAL and TIMESTAMP WITH TIME ZONE lack an exact Parquet primitive. They may be writ