Iceberg vs Parquet: Understanding the Key Differences and When to Use Each Format

In the world of big data, managing and processing large-scale datasets effectively is essential for driving insights and powering data-driven decisions. Two of the most popular file formats in the modern data ecosystem are Apache Iceberg and Apache Parquet. While both serve important roles in the world of data lakes and distributed systems, they are designed for different purposes, and understanding their unique features can help organizations choose the right tool for their needs. In this blog post, we’ll dive deep into the characteristics of both Iceberg and Parquet, comparing their strengths and weaknesses, and exploring when each should be used.

What is Apache Iceberg?

Apache Iceberg is an open-source table format for large-scale analytical datasets. Developed initially at Netflix, it was designed to address the limitations of traditional data lake storage formats. Iceberg is often used in data lakehouses and modern data platforms like Apache Spark, Trino, and Flink.

Key Features of Apache Iceberg:

1. Schema Evolution: Iceberg allows schema changes (e.g., adding or removing columns) without disrupting the existing data. This is a major advantage over older formats like Hive, where schema changes could lead to incompatibilities.
2. ACID Transactions: It supports full ACID transactions, meaning data can be read, written, and modified atomically, ensuring that reads and writes are consistent even in highly concurrent environments.
3. Time Travel: Iceberg provides built-in time travel capabilities. This means users can query historical versions of their datasets, which is useful for auditing, debugging, and restoring data to a previous state.
4. Partition Evolution: Iceberg supports flexible partitioning schemes, and allows partitions to be changed or added over time without rewriting the data. This is crucial for optimizing query performance and accommodating changes in data distribution.
5. Data Lineage: Iceberg tracks the metadata of the dataset, allowing for easier auditing and understanding of the data lifecycle. This is important for traceability and data governance.

What is Apache Parquet?

Apache Parquet is a columnar storage file format that is widely used in big data frameworks. It is designed for efficient data storage and retrieval, especially when dealing with large volumes of complex, semi-structured data. Parquet is also an open-source project, and it is optimized for read-heavy workloads.

Key Features of Apache Parquet:

1. Columnar Storage: Parquet stores data in a columnar format, which makes it highly efficient for analytical queries that only need to read specific columns rather than entire rows of data.
2. Efficient Compression: The columnar format of Parquet allows for better compression, reducing the amount of storage required. Additionally, Parquet supports various compression algorithms like Snappy, GZIP, and LZO.
3. Schema Support: Parquet supports complex data types and nested structures, which makes it suitable for a variety of use cases, from tabular data to JSON-like structures.
4. Optimized for Analytical Queries: Since it’s columnar, Parquet is designed to work efficiently with analytical engines like Apache Spark, Apache Hive, and Presto, which are typically used for reading large datasets in distributed environments.
5. Open and Standardized: Parquet is an open-source project under the Apache Software Foundation and is supported by a wide range of big data processing engines and cloud storage platforms.

Iceberg vs Parquet: A Direct Comparison

Now that we have a basic understanding of both Iceberg and Parquet, let’s compare them directly across a variety of important factors:

1. Storage Model:

• Parquet: Parquet is a file format, meaning that it defines how data is physically stored. It doesn’t manage metadata or the structure of the dataset itself. While it’s great for reading and writing data efficiently, it doesn’t offer built-in tools for managing datasets at scale.
• Iceberg: Iceberg, on the other hand, is a table format that works on top of underlying file formats like Parquet. It manages metadata, schema evolution, partitions, and more. It provides a higher-level abstraction over file formats, which allows it to better handle complex operations like updates, deletes, and schema changes.

2. Schema Evolution:

• Parquet: Parquet supports a rich schema that can handle complex and nested data structures, but it doesn’t handle schema evolution natively. If the schema of the data changes over time, it requires careful management and can lead to compatibility issues.
• Iceberg: Iceberg was designed with schema evolution in mind. It allows you to evolve the schema over time without needing to rewrite or reload the entire dataset. This makes it much easier to manage large datasets that need to accommodate frequent schema changes.

3. ACID Transactions:

• Parquet: Parquet is not inherently transactional. While it works well for batch reads and writes, if you need to perform multiple updates, inserts, or deletes on your data, you would need to manage transactions outside of the Parquet format (using tools like Apache Hudi or Delta Lake).
• Iceberg: Iceberg provides full ACID transaction support, making it ideal for scenarios where you need to perform complex transactional operations on datasets. This is particularly useful in environments where multiple users or processes are interacting with the data simultaneously.

4. Time Travel:

• Parquet: Parquet does not natively support time travel. If you want to query historical versions of data, you would need to manage multiple versions of the data yourself, which can be cumbersome.
• Iceberg: Iceberg supports time travel out-of-the-box. You can query historical versions of the data at any point in time, making it a great option for use cases like auditing, debugging, and rolling back changes.

5. Partitioning:

• Parquet: Parquet doesn’t manage partitioning itself; instead, it relies on external systems (like Hive or Spark) to partition data. Once data is partitioned, it’s up to the user to manage changes to the partitioning scheme, which can be complex.
• Iceberg: Iceberg allows for flexible partitioning and even partition evolution. You can change partition schemes over time without rewriting the data, which is a huge advantage in dynamic environments where data distribution patterns change over time.

6. Performance:

• Parquet: Parquet is highly optimized for read-heavy workloads, particularly for analytical queries that access only a subset of the data (thanks to its columnar format). It also supports efficient compression and encoding, which helps with performance.
• Iceberg: While Iceberg doesn’t directly impact read performance, it enhances it by allowing for more efficient storage and access patterns through its partitioning and schema evolution features. Iceberg ensures that queries are faster by managing metadata and making it easier to optimize storage and partitioning.

When to Use Apache Iceberg vs Parquet

Use Apache Iceberg When:

1. You need ACID transactions and consistency for updates, deletes, and inserts.
2. You require schema evolution without breaking the existing data pipeline.
3. Your dataset requires frequent partitioning changes or you need to optimize large-scale queries.
4. You need to take advantage of time travel capabilities for versioning and data auditing.
5. You are building a data lakehouse and need advanced table management for a large-scale distributed environment.

Use Apache Parquet When:

1. Your use case is focused primarily on analytical querying with large, columnar datasets.
2. You need a highly efficient, columnar storage format for both structured and semi-structured data.
3. You are working with tools like Apache Hive, Apache Spark, or other big data platforms that natively support Parquet.
4. You don’t need complex operations like updates, deletes, or schema evolution at the table level.

Conclusion: Choosing the Right Format

In the end, both Apache Iceberg and Apache Parquet are valuable tools in the big data ecosystem, but they serve different purposes. Parquet is an excellent choice for storing and querying large datasets in a columnar format, especially in analytical and read-heavy environments. Iceberg, on the other hand, provides a higher-level table format that manages metadata, schema evolution, partitioning, and transactions, making it more suitable for complex, large-scale data management operations.

If your needs are primarily around efficient storage and querying of large datasets, Parquet is the clear choice. However, if you need to manage complex data pipelines with schema changes, time travel, and transactional consistency, Iceberg will give you the flexibility and functionality you need.

Ultimately, both formats complement each other. You can use Iceberg to manage the table metadata and schema, while using Parquet as the underlying storage format, benefiting from the best of both worlds.