Apache Parquet is an open source, column-oriented file format designed for efficient storage and retrieval of analytic datasets. Instead of storing each row as a contiguous record (like CSV or JSON lines), Parquet stores values column by column, with rich metadata, encodings, and compression that make scans, filters, and aggregations far cheaper on large tables. Spark, DuckDB, pandas, Trino, BigQuery, Snowflake, and most lakehouse engines can read and write the same Parquet files without a proprietary lock-in.

Why it matters now: AI and analytics teams are moving more of their bronze-to-gold pipelines onto object storage, and the file format underneath those tables decides how much you pay for compute and how fast feature jobs finish. The official Parquet overview frames it as a portable columnar substrate for any processing framework, and vendors such as IBM highlight the same core win: read only the columns you need, compress similar values tightly, and skip irrelevant row groups with statistics.

Columnar Storage vs Row Storage

In a row-oriented file, a query that only needs user_id and event_date still has to pull every other field on those rows from disk. In Parquet, each column is stored and compressed on its own, so the engine can open just those two column chunks. That is the main reason analytic queries on wide tables get dramatically cheaper I/O.

Parquet also stores min/max statistics and other page-level metadata. Query engines use that for predicate pushdown: if a row group’s max date is last month and your filter asks for this week, the whole group can be skipped without reading the data pages.

Encodings and Compression

Because values in a single column tend to look alike, Parquet can apply dictionary encoding, run-length encoding, bit packing, and similar schemes before a general-purpose compressor like Snappy, ZSTD, or GZIP. The Parquet encodings documentation explains how dictionary pages and hybrid RLE/bit-packing shrink repeated categorical fields without losing fidelity. Nested structures are handled with the Dremel-style shredding and assembly algorithm described in the format’s motivation docs, so you can keep complex JSON-like shapes without flattening everything into strings.

Where Parquet Shows Up in Modern Data Stacks

Parquet is the default (or preferred) data file under many open table formats and warehouse export paths. If you already read about how teams organize lakehouse layers, the medallion architecture article is a useful companion: bronze, silver, and gold tables are often Parquet files under Iceberg, Delta, or Hive-style partitions.

Typical patterns include:

• Data lakes and lakehouses. Land events as Parquet, then query them with Spark, Trino, or DuckDB without loading a warehouse first.

• Feature and training datasets. Export labeled frames to Parquet so training jobs can stream columns efficiently.

• Interchange between tools. Write once from pandas or Spark; read everywhere else without CSV schema headaches.

Parquet vs CSV, JSON, and Avro

CSV and JSON are human-readable and easy to debug, but they are row-oriented, poorly typed, and expensive to scan at scale. Avro is also a strong binary format and often preferred for streaming or row-by-row write paths. Parquet shines when the workload is analytic: filter, aggregate, join, and project a subset of columns across millions or billions of rows. Many pipelines use both: Avro or Protobuf on the wire, Parquet on disk for analytics.

Practical Tips for Data Teams

Choose a sensible row-group size. Too small and you pay metadata overhead; too large and you lose skip opportunities and memory efficiency. Most engines default to values that work for multi-GB files.

Partition carefully. Hive-style partitions on high-cardinality columns create tiny files. Prefer partitioning on coarse keys (date, region) and let Parquet’s column stats handle fine filters.

Prefer columnar-aware engines. DuckDB, Spark SQL, and Arrow-based readers can push projections and predicates into Parquet. A naive full-file decode throws away most of the format’s advantage.

Watch schema evolution. Parquet supports adding columns and nullable fields, but merging mismatched schemas across many files still needs discipline in the table layer (Iceberg, Delta, or careful Spark merge options).

Common Mistakes With Parquet

Storing one giant nested JSON blob as a single string column defeats columnar pruning. Writing thousands of tiny Parquet files from micro-batches creates a small-file problem that slows listing and planning. Compressing already-encoded columns with an overly heavy codec can burn CPU for little storage gain. And treating Parquet as a transactional table format by itself is a mistake: concurrency, snapshots, and time travel usually belong in an open table format sitting on top of the files.

If you want to go deeper on building production data and AI systems, explore Coderhouse’s courses below.

• AI Engineering Course — learn to design data-aware AI pipelines that train and serve on governed datasets.

• Data Analytics Course — build the SQL and analytics skills to query columnar datasets efficiently.

FAQ

Is Parquet the same as a database?

No. Parquet is a file format. A database or query engine reads those files (often through a table format) and provides SQL, indexes, and concurrency control.

Can I open a Parquet file in Excel?

Not natively. Use Python (pandas, pyarrow), DuckDB, or a converter to export a slice to CSV when you need a spreadsheet view.

Should every dataset be Parquet?

Prefer Parquet for analytic tables and ML feature stores. Keep CSV/JSON for small configs, human handoffs, and APIs where readability matters more than scan speed.