Apache Parquet

If you have ever waited for a query to scan a giant CSV file and watched the cost meter tick up on your cloud bill, you already understand the problem Apache Parquet was designed to solve. It is the file format quietly powering most modern data lakes, lakehouses, and cloud warehouses, and it has become the default storage layer for analytics in tools like Apache Spark, Snowflake, BigQuery, Amazon Athena, and Databricks.

This guide walks through what Apache Parquet is, how it came to be, how it works under the hood, how it compares to CSV and JSON, and where it fits in a modern data pipeline.

What Is Apache Parquet?

Apache Parquet is an open source columnar storage format used to efficiently store, manage, and analyze large datasets, organizing data in columns instead of rows to improve query performance and reduce storage costs.

Unlike row-based storage formats such as CSV or JSON, Parquet groups all values for a single column together on disk. When a query asks for three columns out of a wide table with two hundred fields, a Parquet reader pulls only those three columns and ignores the rest. This is what makes the Parquet file format so much faster than text formats for analytical workloads.

Apache Parquet is also self-describing. Every file carries its own schema and statistics, which makes it portable across tools and easy to query without any setup. That mix of columnar layout, schema awareness, and built-in compression is why Parquet has become the standard for OLAP workloads in data lakes built on Amazon S3, Azure Data Lake, and Google Cloud Storage.

History of Apache Parquet

Apache Parquet was created in 2013 by engineers at Twitter and Cloudera, based on Google’s Dremel paper, and donated to the Apache Software Foundation.

The Parquet format was born inside two companies dealing with the same headache: huge datasets and analytical queries that touched only a handful of columns. Twitter and Cloudera engineers joined forces, drew on the record shredding and assembly algorithm from Google’s Dremel paper, and built a columnar storage format that worked cleanly on Apache Hadoop and HDFS.

Version 1.0 shipped in 2013, and the project became a top-level Apache Software Foundation initiative shortly after. Over the following decade, Apache Parquet became the storage layer behind nearly every major data lakehouse architecture, including those built on Apache Iceberg and Delta Lake.

Features of Apache Parquet

Apache Parquet’s core features include columnar storage, predicate pushdown, schema evolution, nested data support, built-in compression and encoding, and broad ecosystem compatibility.

A few features explain why Apache Parquet has spread so widely.

Columnar Storage

Parquet stores values column by column rather than row by row. When a query touches only three columns out of two hundred, the engine reads just those three from disk and skips the rest. That single design choice is why analytical queries on Parquet finish in a fraction of the time the same query would take on CSV.

Predicate Pushdown

Every Parquet file carries metadata that includes min, max, and null counts for each column chunk. Query engines use this metadata to skip entire row groups that cannot match a filter, so a query like WHERE country = ‘US’ never opens row groups that hold only EU records. The result is far less I/O and faster results.

Schema Evolution

The Parquet file format tracks schema per file, so columns can be added, removed, or modified over time without rewriting the data already on disk. Old files with the old schema and new files with the new schema sit side by side and are read together, which is what makes Parquet practical for data lakes that grow over years.

Nested Data Structures

Parquet uses the record shredding and assembly algorithm from Google’s Dremel paper to store deeply nested records (structs, arrays, maps) as flat columns and reconstruct them on read. CSV cannot represent nested data at all, and JSON pays a heavy size and parsing penalty trying to.

Compression and Encoding

Apache Parquet pairs encoding (dictionary, run-length, delta) with codecs (Snappy, GZIP, Zstandard) at the page level. Encoding compresses repeated and predictable values, the codec squeezes what is left, and the result is files often a fifth to a tenth the size of the equivalent CSV.

Broad Ecosystem Support

Apache Spark, Apache Hive, Apache Hadoop, Snowflake, BigQuery, Amazon Athena, Databricks, Trino, and Presto all read and write Parquet natively with the same on-disk format. That portability is what keeps teams from getting locked into any single vendor or engine.

How Does Apache Parquet Work?

Apache Parquet stores data in a columnar layout, then organizes it into row groups, column chunks, and pages, with metadata that enables fast, selective reads.

Imagine a table with one billion rows and 200 columns. A row-based format stores all 200 fields of row one, then all 200 fields of row two, and so on. A query that needs only three columns has to read past every other field on disk. Parquet flips this: all values for column A are stored together, then all values for column B, and the reader fetches only the columns the query touches.

That core idea plays out across three nested levels.

Row Groups

A row group is a horizontal slice of the dataset that holds all the data for a subset of rows. Each row group can be read independently, which lets Parquet parallelize work across cores and nodes. A typical row group holds tens of thousands to a few million rows.

Column Chunks

Inside each row group, Parquet stores one column chunk per column. A column chunk contains all values for a single column within that row group, stored contiguously on disk. This contiguity is what allows engines to read only the columns they need.

Pages

Column chunks are split further into pages, the smallest unit of storage in the Parquet file format. Pages are where compression and encoding actually happen, and page-level statistics let readers skip whole pages that cannot match a filter.

Columnar vs Row-Based Storage: What Is the Difference?

Columnar storage saves data column by column, ideal for analytics, while row-based storage saves data row by row, ideal for transactional workloads.

In a row-based format like CSV, all values for one record sit together. Reading a single record is fast, but reading one column across millions of records means touching every record on disk. Columnar storage flips that. Values for one column live together, so a query that aggregates one field reads only that field.

Compression also works better in columnar files. A column of timestamps, currency values, or country codes compresses much tighter than a mixed-type row, because the values share a data type and often repeat. This is why columnar storage formats like Apache Parquet and Apache ORC are the default for analytics, while row-based formats remain better for transactional systems where each interaction reads or writes a full record.

Apache Parquet File Structure

A Parquet file is organized into row groups containing column chunks of pages, with magic numbers and metadata bookending the file for fast lookups.

Every Parquet file starts and ends with a 4-byte magic number, “PAR1,” that marks it as valid. Between those markers sits the actual data, organized into row groups, each containing column chunks, each containing pages.

The file footer stores the metadata: schema, row group locations, column chunk offsets, and statistics for each column. Because the metadata is written at the end after all data is known, Parquet supports single-pass writing while still giving readers a complete map of the file. Readers fetch the footer first, decide what to read, and skip the rest. That design is what makes Apache Parquet efficient on object stores like S3, where seeks are expensive.

Compression and Encoding in Parquet

Apache Parquet supports compression codecs like Snappy, GZIP, and Zstandard, plus encodings like dictionary and run-length encoding, to shrink files and speed up reads.

Compression and encoding work together inside a Parquet file. Encoding transforms raw values into a tighter representation. Dictionary encoding replaces repeated strings with integer IDs. Run-length encoding collapses repeated values into counts. After encoding, a codec like Snappy, GZIP, or Zstandard compresses the bytes further.

Snappy is the common default because it strikes a balance between read speed and file size. GZIP gives smaller files at the cost of slower reads. Zstandard often wins on both fronts on modern hardware. The pairing of column-aware encoding with codec-level compression is why Parquet files routinely come in at a fraction of the size of equivalent CSV or JSON.

Parquet vs CSV vs JSON

Parquet is columnar, compressed, and schema-aware, while CSV is row-based plain text and JSON is row-based with nested structures but no compression.

Parquet is a binary, columnar, schema-aware format built for fast analytical reads on large datasets.

CSV is a row-based plain-text format that is universal and easy to read but has no schema, no compression, and no support for nested data.

JSON is a row-based, semi-structured format that handles nested records well and is common in APIs, but it carries heavy size and parsing overhead at scale.

For analytics at scale, Parquet vs CSV and Parquet vs JSON is rarely close. CSV and JSON keep their place in interchange and small workloads, but the Parquet data format wins on size, speed, and cost wherever queries scan large tables.

When to Use Apache Parquet

Apache Parquet is the right choice for analytics, data lake storage, large-scale ETL, and any workload that reads specific columns from very large datasets.

Cloud Data Lake Storage

Storing analytical data on Amazon S3, Azure Data Lake, or Google Cloud Storage works best when files are small, self-describing, and fast to scan, which is exactly where Parquet shines.

OLAP Queries on Wide Tables

Dashboards and BI tools that scan large fact tables but read only a handful of columns benefit directly from columnar storage and predicate pushdown.

Modern Analytics Engines

Apache Spark, Snowflake, BigQuery, Amazon Athena, Databricks, Trino, and Presto all read and write Parquet natively, so it is the path of least resistance across the modern data stack.

Long-Lived Datasets That Evolve

Schema evolution lets new columns and updated types coexist with historical files, which suits data lakes that grow for years.

Lakehouse Architectures

Apache Iceberg and Delta Lake both use Parquet as the underlying file format, so any team building a lakehouse is already committing to Parquet.

Cost-Sensitive Cloud Setups

Smaller files cost less to store, and engines like Athena and BigQuery price by bytes scanned, so Parquet directly lowers per-query bills.

It is not the right choice for row-level updates with strict latency, real-time event ingestion, or human-readable exports. CSV, JSON, or transactional formats are better there.

What Are the Benefits of Apache Parquet?

Apache Parquet delivers smaller files, faster queries, lower cloud storage costs, schema evolution, and clean integration with the modern data stack.

The Parquet file format benefits stack up across a data pipeline.

• Smaller File Sizes: Columnar layout, encoding, and codec compression combine to produce files that are a fraction of the size of CSV or JSON for the same data. That cuts S3, Azure, and GCS bills directly.
• Faster Query Performance: Predicate pushdown, column pruning, and row-group skipping mean a query touches only the bytes it actually needs. Dashboards refresh faster and analysts wait less.
• Lower Cloud Storage and Compute Costs: Smaller files cost less to store and less to scan. Engines like Athena and BigQuery price by bytes scanned, so columnar Parquet directly lowers per-query bills.
• Schema Evolution Support: Add a new column today without rewriting last year’s data. The Parquet file format tracks schema changes per file, so old and new files coexist cleanly.
• Modern Data Stack Compatibility: Parquet in Spark, Snowflake, BigQuery, Athena, Databricks, Trino, and Presto all behaves the same way. That portability is what keeps teams from getting locked into any single vendor.

Limitations of Apache Parquet

Parquet has real tradeoffs: write overhead from the columnar layout, files that are not human-readable, the small-file problem, and schema changes that require care.

Parquet is built for read-heavy analytics, and that focus comes with limits.

Write Overhead

Writing Parquet is more expensive than writing CSV or JSON. The format has to compute statistics, apply encoding, and compress each column chunk before the file closes. For workloads that write far more often than they read, the upfront cost can outweigh the read-side gains.

Not Human-Readable

A Parquet file is binary, so you cannot open it in a text editor and skim. Inspection requires tools like parquet-tools, DuckDB, or a notebook, which adds friction for teams used to grepping through CSVs.

Compaction and Small File Problem

Pipelines that write many tiny Parquet files (one per micro-batch, for example) end up with thousands of small files in the data lake. Each file carries metadata overhead, and query engines slow down when they have to open thousands of them. The fix is compaction, periodically merging small files into larger ones, which adds operational work.

Schema Changes Require Care

Schema evolution is supported, but it is not free. Renaming columns, changing types, or reordering fields can break readers if done carelessly. Teams need a schema management approach (an Iceberg or Delta table, a contract test in CI) to keep things tidy.

How LatentView Helps Enterprises Build Parquet-Based Data Pipelines

LatentView Analytics helps organizations design and run high-performance data pipelines on Apache Parquet across Databricks, Snowflake, Azure Data Lake, and Amazon S3 lakehouses. We turn fragmented, expensive data flows into clean Parquet-based architectures that scale with the business.

What we bring to the table:

• Lakehouse design on Apache Iceberg and Delta Lake with Parquet as the storage layer, tuned for query performance and cloud cost.
• ETL and ELT modernization that converts legacy CSV and JSON pipelines into compressed, partitioned Parquet datasets ready for OLAP.
• Performance tuning of row group sizing, partitioning strategy, and compression codecs (Snappy, GZIP, Zstandard) to fit your query patterns.
• AI and ML pipeline readiness so the same Parquet store powers BI, ML training, and LLM-driven analytics without duplication.

If you are scoping a data lake build, a Parquet migration, or a lakehouse modernization, a short call with our team is the fastest way to size the opportunity.

Contact us to talk to a LatentView data engineering lead about your Parquet and data lake strategy.

Frequently Asked Questions

1. What is Apache Parquet in simple terms?

Apache Parquet is a columnar file format that stores data column by column instead of row by row, which makes analytical queries faster and files smaller.

2. Is Parquet better than CSV?

For analytics on large datasets, yes. The Parquet file format is compressed, schema-aware, and columnar, while CSV is plain text without compression or types. CSV still has its place in small exports and quick interop.

3. How does Parquet work in Spark?

Apache Spark reads and writes Parquet natively. It uses the file’s metadata to skip row groups, push down filters, and read only the columns referenced in a query, which is why Parquet in Spark is the standard for analytics jobs.

4. What is the difference between Parquet and ORC?

Both are columnar formats. Parquet vs ORC mostly comes down to ecosystem fit. Parquet is the default in Spark, Snowflake, and most cloud lakehouses, while ORC has deeper roots in the Hive ecosystem.

5. What is the difference between Parquet and Avro?

Avro is a row-based format optimized for streaming and write-heavy use cases. Parquet vs Avro: Parquet is for analytics reads, Avro is for event ingestion and inter-system messaging.