Apache Parquet in 2026: The Quiet Format Enters Its Loudest Decade
Thirteen-year-old Apache Parquet faces its busiest development era yet, driven by lakehouse and AI demands: variant types, geospatial support, and footer debates.
Apache Parquet, long the quietest layer of the data stack, is now entering its most consequential development phase in thirteen years. 2026 alone has brought a native variant type for semi-structured data, first-class geospatial types, a new format release, an active redesign of the file footer, proposed types for embeddings and unstructured blobs, a new floating-point encoding, and an eighty-plus-message debate over the future of Parquet versioning itself.
Two forces are driving this surge. The lakehouse era turned Parquet into the shared substrate beneath every table format, meaning every ambition from Iceberg or Delta eventually becomes a requirement on Parquet. Simultaneously, the AI era introduced workloads, embeddings, semi-structured context, wide feature tables, unstructured payloads, that the format's original 2013 design never anticipated. Parquet is effectively being renovated while fully in production, one of the harder and more fascinating engineering challenges in the data ecosystem today.
Structurally, Parquet organizes data into row groups, column chunks, and pages, with a Thrift-serialized footer at the file's end holding schema, location, and min/max statistics that enable query engines to skip irrelevant data efficiently. Much of this year's activity centers on pushing past the limitations of that footer design, since every new Parquet capability arrives as a new logical type, encoding, or statistic.
For engineers, the significance goes beyond internal format mechanics. When specifications like the variant type standardize at the Parquet layer, cross-engine compatibility becomes real, letting Spark write data that Dremio reads transparently. This second act of Parquet's evolution deserves close attention from anyone building on modern data infrastructure.