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What to use parquet or CSV?

History of Parquet File: A Big Data Storage Revolution

The Parquet file format has emerged as a dominant force in the realm of big data storage and analytics. Here's a glimpse into its fascinating journey:

Origins (Pre-2013)

The groundwork for Parquet can be traced back to Apache Trevni, a columnar storage format created by Doug Cutting, the visionary behind Hadoop. Trevni laid the foundation for efficient data storage and retrieval, paving the way for future advancements.

Birth of Parquet (2013)

In July 2013, a collaborative effort between Twitter and Cloudera brought Apache Parquet to life. Designed as an improvement upon Trevni, Parquet aimed for wider adoption and enhanced features.

Initial Release (2013)

The first version, Apache Parquet 1.0, offered significant advantages over traditional row-based formats like CSV. It introduced key features like:

  • Support for Apache Hadoop MapReduce (data processing framework)
  • Integration with Apache Pig (data flow language)
  • Compatibility with Apache Hive (data warehouse software)
  • Ability to work with complex data structures like nested data
  • Support for various compression techniques

Growth and Adoption (2013-Present)

Since its release, Parquet has gained widespread adoption within the big data ecosystem. Here are some key milestones:

  • April 2015: Parquet became a top-level project under the Apache Software Foundation (ASF), signifying its growing importance.
  • Continued development has brought features like efficient dictionary encoding for repeated values and dynamic bit-packing for further compression.
  • Open-source nature and tool compatibility have solidified Parquet's position as a preferred format for big data storage and analytics.

Today, Parquet remains a vital component of the big data landscape, enabling efficient data storage, retrieval, and analysis for various applications.

Parquet vs. CSV: File Formats for Data Storage

While both CSV and Parquet are file formats used for storing data, they take vastly different approaches. Understanding these differences is crucial for choosing the right format for your needs.

Technology Behind Each Format

  • CSV (Comma-Separated Values):

    • Row-Based: Data is stored in rows, with each row representing a single record. Entries are separated by commas (or a similar delimiter), making them human-readable and easy to import/export in spreadsheet programs.
    • Simple Structure: CSV has a basic structure without a defined schema. This means no information about data types or column names is stored within the file itself.
    • Limited Compression: CSV typically doesn't use compression techniques, leading to larger file sizes compared to compressed formats.
  • Parquet (Apache Parquet):

    • Columnar Storage: Data is organized by columns instead of rows. All values for a specific column are stored together, offering significant advantages for data processing.
    • Schema Definition: Parquet files include a schema that defines data types, column names, and optional metadata. This enhances data integrity and simplifies data manipulation.
    • Advanced Compression: Parquet utilizes compression techniques like Snappy or Gzip within columns, leading to smaller file sizes and faster data transfer.

Key Differences

Feature CSV Parquet
Storage Format Row-based Column-based
Schema Definition No schema Includes schema
Compression Limited Efficient compression
Read Performance Slower for specific columns Faster for specific columns
Write Performance Faster Slower (due to schema and compression)
Storage Efficiency Less efficient More efficient
Human Readability Easily readable Not human-readable directly
Scalability Less scalable for large datasets Highly scalable for big data analytics

Choosing the Right Format

  • CSV: Ideal for small datasets, human-readable data exchange, or situations requiring frequent updates/appends.
  • Parquet: Excellent for big data analytics, efficient storage and retrieval of specific data subsets, and scenarios where data integrity and schema enforcement are crucial.

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