apache parquet vs apache iceberg

3 min read 06-12-2024

Choosing the right data lake format is crucial for efficient data storage and processing. Apache Parquet and Apache Iceberg are two popular choices, each with its own strengths and weaknesses. This article will delve into a comparison of these two formats, helping you determine which is best suited for your needs. Both are columnar storage formats designed for analytical workloads, but their approaches to managing evolving data differ significantly.

Understanding Columnar Storage

Before diving into the specifics, let's briefly discuss the benefits of columnar storage. Unlike row-oriented formats like CSV or ORC, columnar formats store data by column. This significantly improves query performance, especially for analytical queries that typically only need a subset of columns. This is because only the necessary columns need to be read from disk, reducing I/O operations and processing time. Both Parquet and Iceberg leverage this efficiency.

Apache Parquet: The Foundation

Apache Parquet is a widely adopted, mature columnar storage format. It's known for its efficient compression and encoding, leading to smaller file sizes and faster query processing. Parquet offers excellent performance for read-heavy workloads.

Parquet Advantages:

Mature and Widely Adopted: Large community support, readily available tools and libraries.
Excellent Compression: Reduces storage costs and improves query performance.
Efficient Encoding: Optimized for different data types, further enhancing performance.
Schema Evolution: Supports schema changes, though this is often handled manually and can lead to complexities.

Parquet Disadvantages:

Limited Data Management: Lacks built-in features for managing evolving datasets (like adding/deleting data). This often requires complex ETL processes.
No ACID Transactions: Data updates can lead to inconsistencies if not carefully managed.
File Management Overhead: Managing a large number of Parquet files can become cumbersome.

Apache Iceberg: Table Management for the Modern Data Lake

Iceberg addresses many of the limitations of Parquet by providing a powerful table management layer on top of storage formats like Parquet (or ORC). It's designed for managing large, evolving datasets in a data lake. Iceberg’s key strength lies in its ability to handle data updates, deletes, and schema evolution efficiently and consistently.

Iceberg Advantages:

Efficient Data Management: Provides built-in capabilities for managing data updates, deletes, and schema evolution. This simplifies data pipelines and reduces the complexity of ETL processes.
ACID Transactions: Ensures data consistency and reliability, even with concurrent updates.
Hidden Partitioning: Iceberg handles partitioning transparently, simplifying query optimization.
Time Travel: Allows querying past snapshots of the data, useful for debugging and auditing.
Improved Query Performance: Leveraging hidden partitioning and optimized metadata management results in faster queries.

Iceberg Disadvantages:

Relatively Newer Technology: Smaller community compared to Parquet, though growing rapidly.
Increased Complexity: The added functionality introduces a layer of complexity compared to Parquet’s simpler structure. This requires a steeper learning curve.

Choosing Between Parquet and Iceberg: A Decision Matrix

The choice between Parquet and Iceberg depends on your specific needs and priorities. Consider the following factors:

Feature	Parquet	Iceberg
Maturity	Mature and widely adopted	Relatively newer, rapidly growing
Data Management	Limited, requires external tooling	Built-in, robust, efficient
Transactions	No ACID transactions	Supports ACID transactions
Schema Evolution	Supported, but manual and complex	Built-in, simplifies schema management
Complexity	Simpler	More complex
Query Performance	Excellent for read-heavy workloads	Excellent, improved by data management features
Cost	Potentially lower storage costs initially	Potentially higher due to metadata overhead

Conclusion: Parquet or Iceberg?

If your data lake has a largely static nature with primarily read-heavy workloads, Parquet's simplicity and performance are compelling. However, if your data is evolving constantly, requiring updates, deletes, and schema changes, and you prioritize data consistency and robust data management, Iceberg's advanced features are hard to ignore. Iceberg often provides better long-term scalability and maintainability, even if it introduces some initial complexity. Consider future scalability needs when making your decision. Many users choose to use Iceberg on top of Parquet, leveraging the best of both worlds.