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

In data engineering today, a single corrupted or maliciously crafted file can compromise entire analytical pipelines, exposing petabytes of sensitive data and bringing critical business operations to a halt. This reality became starkly apparent in 2025 when security researchers discovered CVE-2025-30065, a critical remote code execution vulnerability in Apache Parquet that achieved the maximum CVSS score of 10.0. Yet despite such risks, Parquet remains the cornerstone of modern data infrastructure because its columnar storage architecture delivers compression ratios of up to 16:1 and query performance improvements exceeding 75% compared to traditional formats.

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.

What Are the Key Features of the Parquet File Format?

Parquet is a powerful data-storage format designed to optimize both storage efficiency and query performance. Below are the key features that make Parquet stand out:

• 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.

How Do Row-Based and Columnar Storage Formats Differ in 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, 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 allow the system to fetch only the necessary column values, reducing the amount of data processed and improving performance.

Efficient Compression and Encoding Schemes

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.

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.

What Are the Primary Benefits of Parquet for Data Engineering?

Parquet offers several advantages for data engineering, from reducing storage space to speeding up query performance:

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

What Are the Security Considerations and Encryption Features in Parquet?

Modern data environments demand sophisticated security measures that go beyond traditional whole-file encryption. Parquet's modular encryption framework addresses these needs by enabling column-level data protection while maintaining the format's performance advantages.

Understanding Parquet Modular Encryption

Parquet's modular encryption framework represents a paradigm shift in data security, moving beyond whole-file encryption to enable column-level protection. The system employs a hierarchical key structure where data encryption keys (DEKs) encrypt specific columns or metadata components, while key encryption keys (KEKs) wrap these DEKs. Master encryption keys (MEKs) stored in external key management services (KMS) complete the chain, ensuring sensitive keys never reside in storage systems.

This envelope encryption model allows independent encryption of each column with distinct keys, enabling precise access control where analysts might decrypt only non-sensitive columns without exposing protected data. The architecture supports two cryptographic modes: AES-GCM for full data/metadata authentication and AES-GCM-CTR for lower-latency operations with partial integrity protection.

Critical Vulnerability Awareness

The discovery of CVE-2025-30065, a critical RCE flaw in Apache Parquet's Java library with a CVSS score of 10.0, demands urgent attention from data teams. Exploitation occurs when malicious Parquet files are ingested, enabling arbitrary code execution via schema parsing. While no active exploits exist as of April 2025, mitigation requires three-layered protection:

1. Patch Management: Immediate upgrade to Parquet 1.15.1 eliminates the vulnerability by fixing unsafe deserialization in the parquet-avro module.
2. Input Validation: Pre-ingestion schema scanning using tools like NiFi's PutParquet processor detects anomalies in untrusted files, quarantining suspicious payloads before processing.
3. Encryption Protocols: Modular AES-GCM encryption prevents tampering by cryptographically verifying data integrity without exposing keys to storage systems.

Implementation Patterns and Best Practices

Implementing Parquet encryption requires careful configuration across several dimensions. The footer metadata controls access patterns; encrypted footers completely hide schema details (ideal for sensitive datasets) while plaintext footers allow legacy readers to access unencrypted columns. Column-level encryption specifications demonstrate practical deployment where financial columns receive dedicated keys while non-sensitive attributes remain accessible to broader teams.

Performance overhead varies significantly by algorithm: AES-GCM adds approximately 15% latency due to full authentication but provides tamper evidence, while AES-GCM-CTR incurs only 4-5% overhead by authenticating just metadata. For cloud deployments, KMS integration reduces key management overhead by 40% compared to manual implementations while maintaining compliance with data sovereignty regulations.

How Can You Optimize Parquet for Streaming and Real-time Data Processing?

Streaming data presents unique challenges for Parquet file creation and optimization. Unlike batch processing where large datasets can be efficiently organized into optimal row groups, streaming scenarios require balancing memory constraints, file size optimization, and real-time processing requirements.

Memory-Efficient Streaming Techniques

Writing row-wise streams to columnar Parquet requires buffering 128MB-1GB row groups, creating memory pressure in streaming environments. Traditional approaches either generate numerous small files (harming query performance) or consume excessive memory during buffering. Modern solutions employ two-pass writing strategies that significantly reduce memory overhead.

The two-pass approach works by initially writing micro-row groups (10MB) to disk, then using background compaction processes to merge them into optimal 128MB groups. This technique reduces peak memory usage by up to 89% for IoT data streams while maintaining query performance characteristics. Streaming platforms like Kafka consumers can leverage maxPollRecords tuning to optimize in-memory row grouping before Parquet writes.

Advanced Performance Optimization Techniques

Recent advancements in Parquet processing include vectorized V2 encodings that deliver up to 77% performance improvements. Delta encodings (DELTA_BINARY_PACKED, DELTA_LENGTH_BYTE_ARRAY) coupled with vectorized readers accelerate analytics by reducing CPU cycles through efficient column-wise operations.

Cloud-native partitioning strategies further enhance streaming performance. Temporal hierarchies using date-based partitioning (year=2025/month=07) minimize S3/GCS list operations during scans, while column-metadata indexing auto-generates Min/Max statistics for Parquet footers, accelerating predicate pushdown in Spark SQL. These optimizations typically achieve 33% smaller files and 11% faster queries versus unpartitioned layouts.

Real-time Integration Patterns

Event-driven architectures successfully embed Parquet within stream processing through several patterns. Kafka Connect Parquet sinks write Avro-decorated records directly to Parquet via specialized connectors, leveraging Schema Registry for schema validation. Delta Lake's Auto-Optimize feature merges streaming Parquet micro-files into larger partitions (≥128MB), balancing write latency with read efficiency.

For high-throughput scenarios, IoT pipelines using these patterns achieve 35ms median latency at 50,000 events per second. The key lies in intelligent batching windows (typically 5 seconds) that accumulate sufficient data for efficient Parquet row group creation while maintaining near-real-time characteristics essential for operational analytics.

How Should You Work with Parquet in Practice?

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.

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 such as 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

Modern ELT platforms like Airbyte have significantly advanced their Parquet integration capabilities. Recent developments include enhanced schema evolution handling, 40-60% improved write speeds through advanced compression techniques, and intelligent incremental sync capabilities. Airbyte's latest S3 destination connector updates support Zstandard compression, tunable row group sizes, and automated schema management that adapts to structural changes in source data without manual intervention.

For CDC pipelines, Airbyte's "Append + Deduped" sync mode writes updated records as new Parquet files while maintaining manifest-based deduplication in downstream processing. These innovations eliminate friction in moving data between storage and compute layers while maintaining enterprise-grade performance and reliability.

What Are the Best Practices When Working With Parquet?

• 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.
• Implement Security Scanning – Pre-validate Parquet files from untrusted sources and maintain current patch levels to prevent security vulnerabilities.
• Optimize for Workload Type – Use Zstandard level 3 for analytical storage, Snappy for real-time ingestion, and configure row group sizes based on query patterns.

How Does Parquet Fit into 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. Modern implementations leverage Parquet's modular encryption for granular security while maintaining the columnar efficiency essential for large-scale analytics.

How Does Parquet Compare to Other Data Formats?

Choosing the right data format for your workload can significantly impact both storage efficiency and query performance. Below, Parquet is compared with other popular data formats like ORC, Avro, and CSV:

Suggested read: Avro vs Parquet

Quick Comparison Table

What Are the Primary 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 and enables efficient embedding storage for AI workloads.
• Streaming Data Pipelines – Convert Kafka or Kinesis streams into partitioned Parquet files for downstream batch analytics.
• Regulatory Compliance – Financial institutions leverage columnar encryption for compliance with data sovereignty regulations, encrypting customer identifiers differently per jurisdiction while keeping transaction data globally accessible.
• Observability Engineering – Parquet optimizes metrics storage for distributed tracing and cost-effective log retention, achieving 10x cost reduction over traditional approaches.

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, implementing secure column-level encryption for compliance requirements, or building memory-efficient streaming pipelines, Parquet provides the foundation for modern data architectures. The format's continued evolution, including advanced security features and performance optimizations, ensures its relevance as data scales toward exabyte environments.

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

Frequently Asked Questions

When should I not use Parquet as my file format?

‍Parquet excels in analytical workloads where you read specific columns from large datasets. However, it’s not ideal for transactional use cases or frequent row-level updates, as it’s optimized for read-heavy, append-only scenarios. If you need fast record-level writes or binary serialization for streaming systems, consider formats like Avro or Protobuf instead.

Can Parquet be used effectively outside of the cloud?

‍Absolutely. While Parquet is heavily used in cloud-native environments like S3 or GCS, it also performs well on on-premise systems such as Hadoop HDFS or local SSD-based clusters. Many organizations use Parquet with Spark, Hive, or Trino in hybrid environments where compute is local but storage may be cloud or edge-based.

How do I handle schema changes safely with Parquet?

‍Parquet supports schema evolution, allowing you to add new columns or change column order without rewriting existing files. However, changes to data types or deleting columns can break compatibility, so it's important to version schemas and validate downstream readers. Tools like Delta Lake or Iceberg help manage schema changes reliably through metadata logs.

Is Parquet a secure format by default?

‍Not by default. Security in Parquet requires active configuration. Its modular encryption framework allows for column-level protection, but you must enable encryption and integrate it with a key management system (KMS). For best results, combine encrypted Parquet files with input validation, patch management, and access control to create a secure data pipeline.