Apache Parquet
.parquet.pqAny data pipeline writing columnar analytics data — Python/pandas, Spark, big-data/ETL tools
What it is
A column-oriented binary format built for large analytics datasets. AltaiPlot reads it via a bundled Python bridge that delegates directly to pandas' read_parquet (backed by Apache Arrow's own mature parquet codec) — not a hand-written parser.
Who produces it
Data engineering and analytics pipelines (pandas, Spark, dbt, cloud data warehouses) that export intermediate or final results in Parquet instead of CSV for size/speed reasons.
What we read / what we don't read
What we read
- All columns and rows in the file, via pandas.read_parquet
What we don't read
- This path hasn't been independently verified against a real-world third-party Parquet file this session — it inherits pandas/Arrow's own well-established parquet support, but AltaiPlot's own test coverage so far only round-trips a file the same library wrote, not one produced by an external tool. Treat it as spec-compliant rather than field-verified until we've confirmed otherwise
How to import it into AltaiPlot
- Open AltaiPlot and drag your .parquet/.pq file onto the window, or use File → Import.
- AltaiPlot detects the Parquet magic bytes (PAR1) and hands it to the bundled Python reader automatically.
- All columns land as plotting-ready data — no manual schema mapping needed.
Auditability note: this page's capability claims are sourced fromresources/python/app_sidecar.py:1579 — see the full format list for every extension AltaiPlot recognizes.