# Apache Parquet: How Columnar Storage Speeds Analytics > Apache Parquet is a columnar file format that reduces analytical reads, improves compression, and lets query engines skip irrelevant row groups. Source: https://hyperlake.cloud/blog/what-is-apache-parquet Published 2026-10-14 · by Hyperlake Team · Hyperlake Video: [Watch: What is Apache Parquet (1:57)](https://www.youtube.com/watch?v=wslF3-Rqx6c) Apache Parquet is a self-describing, columnar file format built for analytical workloads. By storing values from the same column together, it lets query engines read only the required columns, compress similar data efficiently, and skip irrelevant groups of rows using statistics embedded in the file metadata. This matters because analytical tables often contain many columns while individual queries use only a few, making physical storage layout a major factor in I/O and processing demand. The video above walks through the core ideas. ## What is Apache Parquet? Apache Parquet is an open file format that organizes tabular data by column instead of storing every row as one contiguous unit. It is designed for analytical systems that filter, scan, and aggregate large datasets. Consider a sales table with 50 columns. A report calculating monthly revenue by region may require only the date, revenue, and region columns. A row-oriented layout places every field for each record together, while Parquet allows an engine to retrieve the three required columns without reading the other 47. Parquet divides a file into row groups. Within each row group, values are organized into separate column chunks, which are further divided into pages for encoding and reading. This structure supports column-level access while keeping related ranges of rows together. Parquet primarily suits analytical workloads. Row-oriented storage may be more appropriate for transaction processing that frequently retrieves or changes complete individual records. ## How does Parquet reduce data reads and improve compression? Parquet reduces reads through column projection: a query engine loads only the columns needed by the query plan. It also improves compression because each column contains values with the same data type and often similar or repeated values. For example, a revenue-by-region query can read three columns from a much wider table. Unselected customer notes, shipping details, and product descriptions do not need to be transferred, decoded, or held in memory. Keeping like values together also exposes patterns that encodings and compression codecs can exploit. A timestamp column has a consistent representation, while a region column may repeat a small set of values. These columns generally compress more effectively than rows mixing strings, numbers, booleans, and timestamps. Encoding and compression are distinct. Encoding represents values compactly using methods such as dictionary or run-length encoding, while a compression codec reduces the resulting byte stream. Actual results depend on value distribution, ordering, cardinality, codec choice, and the software writing the file. These properties make Parquet a common component of [data lakehouse architecture](https://hyperlake.cloud/blog/data-lakehouse-architecture-explained-the-four-layers-that-make-it-work), where engines need to scan large analytical datasets efficiently. ![Diagram: Row storage keeps complete records together, while Parquet reads and compresses values by column.](https://hyperlake.cloud/blog/img/production/9291cfc2a396ff6593d394c2e9ea4b8814812b08-1200x750.png?w=1600&fit=max&auto=format) *Parquet avoids processing unselected columns and groups similar values for compression.* ## How does Parquet skip irrelevant row groups? Parquet stores column statistics in its metadata, commonly including minimum values, maximum values, and null counts for each row group. Query engines can inspect these statistics and skip groups whose value ranges cannot satisfy a filter. Suppose a query requests transactions from July. If a row group’s minimum and maximum dates show that it contains only May records, the engine can reject that group without reading or decompressing its data pages. This is often discussed as predicate pushdown and, at the storage layer, row-group pruning. The process has four steps: 1. The engine translates the query filter into a predicate. 1. It reads the Parquet footer and relevant statistics. 1. It excludes row groups that cannot contain matching values. 1. It opens the required columns in the remaining groups. Statistics show whether a group might match, not whether every row does. If a requested value falls between a group’s minimum and maximum, the engine must still read and filter that group. Pruning works best when the data layout produces useful value ranges. ![Diagram: A query predicate is checked against Parquet statistics before matching row groups and columns are opened.](https://hyperlake.cloud/blog/img/production/b04503e065f9d3fe1c449e83ce6539b5fb5aacc8-1200x750.png?w=1600&fit=max&auto=format) *Metadata lets an engine reject impossible row groups before decompression.* ## Why is Parquet used in data lakehouses? Parquet combines selective column reads, efficient compression, metadata-driven pruning, and an embedded schema. These characteristics make it a common physical file format for analytical data in object storage. The file footer contains schema information alongside statistics and other metadata. A compatible reader can identify an individual file’s fields and data types without an external schema registry. A multi-file table may still need a catalog or table format to manage file locations, partitions, schema evolution, and snapshots. Open table formats such as Apache Iceberg commonly use Parquet for data files. Parquet defines how values are represented inside each file; Iceberg manages how collections of files form a consistent table over time. This distinction is explained further in [what Apache Iceberg is](https://hyperlake.cloud/blog/what-is-apache-iceberg). Parquet alone does not provide table transactions or coordinated metadata changes. The surrounding table format, catalog, storage system, and query engine supply those broader platform capabilities. ## Key takeaways - Apache Parquet stores tabular data by column, which benefits analytical queries that use only a subset of fields. - Similar values within each column support effective encoding and compression. - Embedded statistics help engines skip row groups that cannot match a query predicate. - Parquet includes file-level schema metadata but does not replace a table format or catalog. - Data lakehouses often combine Parquet files with a table-management layer such as Apache Iceberg. ## How Hyperlake helps Hyperlake can assemble and operate governed data foundations using Iceberg and object storage with a fitting query engine, depending on the workload and deployment. It combines data services with identity, access policy, observability, audit, and lifecycle controls in infrastructure controlled by the organization or its client. To discuss a Parquet-based analytical environment, [talk to our team](https://hyperlake.cloud/contact). ## Frequently asked questions ### Is Apache Parquet a database or a file format? Apache Parquet is a file format, not a database or query engine. It specifies how columnar data, schemas, encodings, compression, and statistics are represented inside files. Databases, lakehouse table formats, and analytical engines can use Parquet as one component of a larger data platform. ### Does a Parquet file need an external schema registry? A compatible reader does not need an external schema registry to identify the schema of an individual Parquet file because that schema is embedded in the file metadata. A dataset containing many files may still need a catalog or table format to coordinate discovery, partitions, schema changes, and snapshots. ### Are Apache Parquet and Apache Iceberg the same thing? No. Parquet is a physical file format that defines how data is stored within individual files. Apache Iceberg is a table format that tracks collections of data files and adds table-level capabilities such as snapshots, schema evolution, and consistent metadata management. An Iceberg table can use Parquet as its data file format.