Parquet File Format: Efficient Data Storage for Analytics

TL;DR — Parquet is an open-source, columnar storage file format optimized for big data analytics. It stores data by column rather than by row, enabling significant compression, faster query performance for analytical workloads, and efficient data serialization in distributed systems.

In the world of big data, the choice of file format profoundly impacts storage costs, query performance, and overall pipeline efficiency. While flat files like CSV are ubiquitous, they quickly become bottlenecks for analytical workloads at scale. Data engineers constantly seek formats that balance storage optimization with rapid data retrieval.

This article dives into the Parquet file format, an industry standard designed to address the challenges of modern data lakes and warehouses. We’ll explore its columnar architecture, compare its strengths against other formats, discuss its practical applications, and demonstrate how Kestra orchestrates workflows to leverage Parquet’s benefits for your data operations.

How Parquet File Format Works

Apache Parquet originated from the collaboration between Twitter and Cloudera, designed to be a more efficient data representation for the Hadoop ecosystem. Its core innovation lies in its columnar storage model, which fundamentally changes how data is written to and read from disk.

Columnar vs. Row-Oriented Storage: A Fundamental Difference

To understand Parquet, you must first grasp the distinction between row-oriented and columnar storage.

• Row-Oriented Storage (e.g., CSV, JSON, Avro): Data is stored row by row. A database or file system writes all fields of the first record, then all fields of the second, and so on. This is efficient for transactional workloads where you need to retrieve entire records (e.g., SELECT * FROM users WHERE user_id = 123).

• Columnar Storage (e.g., Parquet, ORC): Data is stored column by column. The system writes all values for the user_id column, then all values for the email column, and so on. This is highly efficient for analytical queries that only need a subset of columns (e.g., SELECT AVG(purchase_amount) FROM sales). The query engine can read only the purchase_amount column, ignoring all others and drastically reducing I/O.

Key Characteristics of Parquet

Parquet’s design is built on several key features that make it a cornerstone of modern data platforms:

• Columnar Storage: As explained above, this is the primary driver of its performance benefits for analytics.
• Compression: Because data within a column is of the same type (e.g., all integers or all strings), it can be compressed much more effectively than mixed-type data in rows. Parquet supports various compression codecs like Snappy, Gzip, and ZSTD.
• Schema Evolution: Parquet stores the schema within the file itself. This allows for schema evolution, where you can add, remove, or alter columns over time without breaking downstream consumers.
• Nested Data Structures: Parquet has a rich type system that supports complex, nested data structures. It uses the Dremel record shredding and assembly algorithm to represent these structures efficiently.
• Predicate Pushdown: Query engines can push filtering logic down to the storage layer. This means rows can be filtered out based on metadata stored in the Parquet file footers without even reading the data blocks, further accelerating queries. This is a key feature in systems like Apache Iceberg, where data files are often stored in Parquet format.

Why Parquet is Essential for Modern Data Architectures

The shift from transactional databases to large-scale data lakes and analytics platforms has made Parquet an indispensable tool. Its architecture directly addresses the pain points of processing terabytes or petabytes of data.

Performance Advantages for Analytics and Query Engines

For analytical queries, Parquet offers significant performance gains:

1. Reduced I/O: By reading only the necessary columns, query engines minimize the amount of data read from disk or cloud storage. This is a game-changer for wide tables with hundreds of columns.
2. Higher Compression Ratios: Columnar data homogeneity leads to better compression, reducing storage footprint and costs, especially in cloud environments where storage is a recurring expense.
3. Vectorized Execution: Modern query engines like DuckDB, Polars, and those in Spark or Snowflake can leverage columnar data to perform operations on entire columns at once (vectorized processing), which is much faster than row-by-row iteration.

Common Use Cases for Parquet

Parquet is the de facto standard in many big data scenarios:

• Data Lakes: It is the most common file format for storing structured data in cloud data lakes on Amazon S3, Google Cloud Storage, or Azure Data Lake Storage.
• ETL/ELT Pipelines: In a typical data pipeline, raw data might be ingested as JSON or CSV, then transformed and stored as Parquet for efficient downstream processing and analytics. This is a core pattern in modern ETL workflows.
• Machine Learning: ML training jobs often require reading specific features (columns) from massive datasets. Parquet allows for efficient loading of just these features.
• Data Archiving: Its high compression ratio makes it a cost-effective choice for long-term data storage. Kestra’s internal data storage system leverages similar principles for efficient data handling between tasks.

Parquet vs. Other Data Formats: Choosing the Right Tool

While Parquet is powerful, it’s not the only option. Understanding its trade-offs against other formats is crucial for making informed architectural decisions.

Parquet vs. CSV: A Clear Winner for Big Data

For small datasets or simple data exchange, CSV is fine. For analytics at scale, Parquet is almost always the better choice.

Parquet vs. Avro and ORC: Specialized Columnar Formats

• Avro: Also an Apache project, Avro is a row-based format with excellent support for schema evolution. It’s often preferred for streaming data and Kafka pipelines, where individual records are processed. Its row-based nature makes it faster for writes and for retrieving entire records.
• ORC (Optimized Row Columnar): Another columnar format that originated from the Hive project. ORC and Parquet are very similar in performance and features. ORC often has slightly better compression and performance in the Hive/Presto ecosystem, while Parquet has broader adoption, especially within the Spark community.

The choice between them often comes down to the primary processing engine and specific workload needs. Kestra’s serialization plugins support all these formats, allowing you to convert between them as needed.

Orchestrate Parquet Workflows with Kestra: From CSV to Columnar

In many data platforms, a common task is converting raw, row-based data into optimized Parquet files. Kestra automates this process declaratively, ensuring reliability and observability. The workflow below downloads a CSV file, converts it to Parquet, and makes it available for downstream tasks.

Kestra uses an internal data format called Amazon ION to facilitate conversions. This workflow first converts the CSV to ION, then ION to Parquet. You can learn more about why Kestra uses ION in our detailed blog post.

id: csv-to-parquet-conversion
namespace: company.team.data

tasks:
  - id: download_csv
    type: io.kestra.plugin.core.http.Request
    uri: https://raw.githubusercontent.com/kestra-io/datasets/main/csv/customers.csv

  - id: csv_to_ion
    type: io.kestra.plugin.serdes.csv.CsvToIon
    from: "{{ outputs.download_csv.uri }}"
    header: true

  - id: ion_to_parquet
    type: io.kestra.plugin.serdes.parquet.IonToParquet
    from: "{{ outputs.csv_to_ion.uri }}"

triggers:
  - id: daily_schedule
    type: io.kestra.plugin.core.trigger.Schedule
    cron: "0 8 * * *"

This automated workflow provides several benefits:

• Declarative Conversion: The entire process is defined in simple, reviewable YAML. There are no hidden scripts.
• Format Flexibility: Kestra’s plugin-serdes can handle a wide range of formats. You can easily adapt this flow to process JSON, Avro, or XML. See our ZIP CSV to Parquet blueprint for a more advanced example.
• Scheduling and Automation: The Schedule trigger ensures this conversion runs daily without manual intervention.
• Error Handling and Retries: Kestra’s orchestration engine provides built-in retries and error handling, making the pipeline robust.
• Data Lineage: The UI provides a clear view of the data flow, showing how the raw CSV is transformed into the final Parquet file.

Working with Parquet Files in Practice

Since Parquet is a binary format, you can’t simply open it in a text editor. You need specific tools to interact with it.

Tools to Open and Inspect Parquet Files

• Python: Libraries like pandas (with pyarrow or fastparquet engines) make it easy to read Parquet files into a DataFrame: pd.read_parquet('file.parquet').
• DuckDB: An in-process analytical database that can directly query Parquet files (local or remote) with high performance. This is great for exploration and transformation, as shown in our Parquet to Excel with DuckDB blueprint.
• Apache Spark: The primary tool for large-scale distributed processing of Parquet files.
• Cloud Query Services: AWS Athena, Google BigQuery, and Snowflake can all query Parquet files directly from cloud storage, making them a powerful combination for building a serverless data lake. This approach is often used as a modern alternative to traditional Snowflake Tasks.

The Readability Challenge: Binary by Design

The lack of human readability is an intentional trade-off. Parquet prioritizes machine efficiency, compression, and performance over human convenience. For debugging, engineers typically use the tools above to read a sample of the file into a more readable format or rely on the metadata and schema stored within the file.

Addressing Parquet’s Limitations

Despite its strengths, Parquet is not a universal solution.

When Write Performance Matters

Writing Parquet files can be more computationally intensive than writing simple text files like CSV. The process involves buffering rows, organizing them into columnar chunks, compressing them, and writing metadata. For write-heavy, transactional workloads where individual records are frequently appended, a row-based format like Avro may be more suitable.

Considering Alternatives for Specific Needs

• Streaming Data: For real-time event streams in systems like Kafka, Avro is often the preferred format due to its fast serialization/deserialization and robust schema evolution capabilities.
• Mutable Data: If your use case requires frequent updates to individual records, a file format is likely not the right choice. Transactional databases or specialized data formats like Delta Lake or Apache Hudi (which use Parquet for the underlying data but add a transactional layer) are better suited. If a pipeline fails midway, Kestra’s ability to replay an execution from the point of failure can help manage state without requiring mutable data formats.

Where Parquet Pays Off

Parquet excels in scenarios where read performance and storage efficiency are paramount. Its adoption is widespread because it directly supports the goals of modern analytics:

• Efficiently storing vast datasets in data lakes on cloud storage.
• Accelerating analytical queries in data warehouses and query engines.
• Optimizing data transfer and processing in large-scale ETL pipelines.
• Preparing data efficiently for machine learning model training.
• Building cost-effective data ingestion and processing systems, such as the one in our Hacker News data ingestion blueprint.

Related concepts

• ETL vs. ELT
• Lakehouse Architecture
• Data Orchestration
• Data Storage in Kestra
• Serialization and Deserialization
• File Compression