A Deep Dive into Parquet: The Data Format Engineers Need to Know

TL;DR
Parquet is an open-source, columnar storage file format (officially under the Apache Software Foundation) that delivers highly efficient data compression, saves storage space, and speeds up analytical queries by scanning only the necessary columns. For OLAP workloads, it routinely outperforms row-based file formats such as CSV or JSON—often cutting data scanned by 10×.

In this article, you’ll learn what the Parquet data format is, how it handles complex data while optimizing data storage, and where it shines inside modern cloud data lakes and lakehouse architectures. We’ll also cover best practices for creating, storing, and querying Parquet files at scale.

Understanding Parquet

Apache Parquet is a columnar storage file format widely used in big data processing and analytics. Originally created by Cloudera and Twitter in 2013, it is now part of the Apache Hadoop ecosystem and a first-class citizen in data processing frameworks such as Apache Spark, Hive, Presto/Trino, and most cloud data warehouses.

Parquet organizes data into row groups and column chunks so engines can fetch specific column values without reading the entire file. This columnar storage format improves compression ratios, lowers I/O, and dramatically accelerates analytics on large datasets stored in Amazon S3, Google Cloud Storage, or Azure Data Lake Storage.

Parquet integrates seamlessly with data processing frameworks like Apache Spark, Hive, Presto, and serverless engines such as AWS Athena and BigQuery—making it the de facto storage format for big data and data lakes.

Key Features of the Parquet Format

Parquet is a powerful data storage format designed to optimize both storage efficiency and query performance. In this section, we’ll explore the key features that make Parquet stand out, including its columnar storage structure, flexible compression options, advanced encoding schemes, and support for schema evolution. These capabilities enable Parquet to handle large datasets with ease, reduce storage costs, and speed up data processing, making it an ideal choice for big data environments.

• Columnar storage format: Data is written by column, enabling engines to read only the relevant data and skip non-relevant data.
• Flexible compression options: Supports Snappy, Gzip, LZO, Brotli, and Zstandard, giving teams the freedom to balance CPU vs. cloud storage costs.
• Advanced encoding schemes: Dictionary encoding, run-length encoding, bit-packing, and delta encoding further reduce data files and boost decompression speed.
• Rich metadata: Min/max statistics per column chunk empower predicate pushdown and automatic schema inference.
• Predicate pushdown: Filter conditions are evaluated at the storage layer, reducing data scanned and accelerating query execution.
• Schema evolution: Add or modify columns without rewriting all the data; Parquet can seamlessly support schema evolution in production pipelines.
• Nested & complex data types: Arrays, maps, and structs let you handle complex data natively, eliminating costly JSON flattening.
• Broad interoperability: Works across programming languages (Python, Java, Rust, Go) and platforms, including Delta Lake and Apache Iceberg table formats.

Row-Based vs. Columnar Storage Formats: Optimizing Data Storage and Processing

When selecting the right data storage format for your datasets, it's essential to understand the key differences between row-based formats and columnar formats like Parquet. In row-based formats, each row of data is stored together, which can be efficient for transactional operations but less effective for analytical queries. In contrast, columnar formats like Parquet store data by columns, enabling more efficient querying and compression.

How Columnar Formats Improve Data Processing

In row-based formats, when processing data, the system must read the entire row, even if only specific columns are relevant for the query. This leads to unnecessary data scans, higher storage consumption, and slower performance. Columnar formats, however, allow the system to fetch only the necessary column values, reducing the amount of data processed and improving performance.

Efficient Compression and Encoding Schemes

Columnar formats also excel when it comes to compression and encoding. By grouping similar data types together, Parquet can apply encoding schemes such as dictionary encoding and run-length encoding, which reduce storage space and improve query performance. These techniques are not as effective in row-based formats, which store disparate data types together, leading to less efficient compression.

Flexibility with Data Schema

Another advantage of columnar formats is their ability to support schema evolution. In row-based formats, changing the data schema often requires rewriting large portions of data. Columnar formats like Parquet can easily adapt to changes, allowing new columns to be added without the need to rewrite the entire row..

The Benefits of Parquet for Data Engineering

Parquet offers several advantages for data engineering, from reducing storage space to speeding up query performance. Its columnar format and flexible compression options make it ideal for efficiently handling large datasets, complex data, and optimizing data storage.

1. Efficient I/O Operations

Columnar storage means engines read only the required columns—reducing disk scans, network transfer, and CPU usage.

2. Better Compression & Lower Storage Space

Storing the same data types together yields highly efficient data compression. Many teams report 2-5× smaller footprints than CSV files, directly cutting cloud storage space and egress bills.

3. Improved Query Performance

Column pruning plus predicate pushdown drastically lowers the amount of data scanned, turning minute-long queries into seconds.

4. Future-Proof Schema Evolution

Need to add a new metric tomorrow? Parquet supports backward- and forward-compatible schema changes without re-exporting petabytes of data.

5. Ecosystem Ubiquity

From Snowflake to Databricks and open-source Trino, nearly every analytical engine speaks the Parquet file format—enabling straightforward data interchange across different data files and tools.

Real-World Performance Gains

Working with Parquet: A Practical Guide

Now that you understand the benefits and key features of the Parquet file format, it's time to explore how to effectively create, store, and read Parquet files in your data workflows. 

This section provides a step-by-step guide to working with Parquet, from setting up your environment and writing Parquet files, to reading and processing them for efficient data analysis. 

Creating Parquet Files

1. Select a language or framework with Parquet write support.
2. Convert or ingest your structured data tables into DataFrame objects.
3. Use APIs such as pandas.DataFrame.to_parquet() or spark.write.parquet() to write Parquet files.
4. Configure compression and encoding (e.g., compression="snappy").
5. Store them in cloud storage (S3, Google Cloud Storage, Azure Data Lake Storage) or on-prem HDFS.

Reading Parquet Files

1. Pick any Parquet-aware engine—Python (Pandas, PyArrow), Spark, Hive, Trino.
2. Call read_parquet() or equivalent, pointing to a file path or folder containing multiple Parquet files.
3. Apply filters (e.g., WHERE clause) so only relevant data is read.
4. Process, visualize, or push downstream.

Data Integration & Ecosystem Compatibility

Most ELT tools—including Airbyte—can convert raw CSV files or streaming data into the Parquet format automatically, ensuring friction-free movement between storage and compute layers.

Best Practices When Working With Parquet

To make the most of the Parquet file format, it’s essential to follow best practices that maximize its efficiency and performance. From managing file sizes and row groups to leveraging advanced encoding schemes, these practices ensure that you get the best storage space savings, query speed, and flexibility in your data processing workflows.

• File & Row-Group Size: Target 128 MB–1 GB files; set row groups to 64–256 MB for balanced parallelism.
• Partition Wisely: Use moderate-cardinality keys (year/month/day, region) so each partition still holds sizable files.
• Choose Compression Codecs: Default to Snappy for speed; switch to Zstandard when cloud storage costs dominate.
• Enable Dictionary Encoding: Perfect for categorical columns with repeated values.
• Leverage Column Pruning & Predicate Pushdown: Filter early to avoid reading non-relevant data blocks.
• Avoid “Wide” Schema Changes: Plan schema evolution to prevent costly rewrites across thousands of column chunks.
• Monitor Performance Metrics: Track query latency and data scanned to surface optimization opportunities.
• Compact Small Files: Streaming data can create many tiny files; schedule compaction jobs to merge them.

Parquet in Modern Lakehouse Architectures

Parquet is more than a standalone storage format—it underpins emerging table formats and in-memory standards:

• Delta Lake uses Parquet files plus a transaction log to add ACID guarantees, time travel, and upserts.
• Apache Iceberg layers snapshot isolation and partition evolution on top of Parquet data files.
• Apache Arrow offers a columnar, zero-copy in-memory representation that reads Parquet directly for high-speed analytics.

These technologies let teams build lakehouses that combine the scalability of data lakes with the governance of traditional warehouses.

Parquet vs. Other Data Formats

Choosing the right data format for your workload can significantly impact both storage efficiency and query performance. In this section, we’ll compare Parquet with other popular data formats like ORC, Avro, and CSV. By highlighting key differences in storage style, compression capabilities, and schema evolution support, we’ll help you understand why Parquet is often the preferred choice for modern data lakes and big data processing tasks..

Suggested read: Avro vs Parquet

Quick Comparison Table

‍Use Cases and Examples

• Data Lakes & Lakehouses: Airbnb, Uber, and Fortune 500 retailers rely on Parquet in S3 or Azure to store big data efficiently while enabling fast SQL analytics.
• BI & Dashboards: Tools such as Tableau, Apache Superset, and Power BI query Parquet through engines like Trino, reducing data scanned costs.
• Machine-Learning Feature Stores: Loading only the specific column values required for model training speeds iterations.
• Streaming Data Pipelines: Convert Kafka or Kinesis streams into partitioned Parquet files for downstream batch analytics.

Elevate Your Modern Data Engineering with Parquet 

The Apache Parquet format has become the backbone of efficient storage and retrieval in big-data ecosystems. Its columnar storage, compression, and encoding schemes, and support for schema evolution allow engineers to store large datasets cost-effectively, query only relevant data, and future-proof pipelines against change. 

Whether you’re optimizing data storage for petabyte-scale data lakes or converting data from row-oriented databases to accelerate analytics, Parquet remains a reliable, high-performance choice.

Keep exploring the latest in data engineering on our blog, and start turning your raw data into actionable insight with Parquet files today.