Delta vs. Parquet: A Deep Dive into Big Data Storage Solutions

Unlocking the intricacies of big data storage solutions is pivotal in today’s data-driven landscape. As organizations grapple with vast amounts of data, choosing between storage formats like Delta and Parquet becomes crucial. Diving deep into their technical nuances, this article highlights why Delta is emerging as the preferred choice for many. From ACID transactions to schema evolution, discover the game-changing features that set Delta apart in the competitive world of data storage.

Photo by Lesly Derksen on Unsplash

1. Introduction to Delta and Parquet

Parquet: An open-source columnar storage format developed under the Apache Software Foundation. It is designed to be compatible with a wide variety of data processing tools in the Hadoop ecosystem.

  • Encoding: Uses dictionary encoding, run-length encoding, and bit-packing.
  • Compression: Supports multiple codecs like Snappy, Gzip, and LZO.
  • Integration: Native integration with Hadoop, HivePresto, and more.

Delta: Delta Lake is more than just a file format; it’s a storage layer that brings ACID transactions to big data workloads on top of Spark.

  • Underlying Storage: Uses Parquet for physical storage but adds a transaction log.
  • Log Structure: Maintains a transaction log to keep track of commits, ensuring data integrity.

2. Technical Differences

a. ACID Transactions:

  • Delta: Uses a combination of snapshot isolation and serializability to ensure consistency. The transaction log keeps track of every change, ensuring that operations are atomic, consistent, isolated, and durable.
  • Parquet: Doesn’t have built-in transactional capabilities.

b. Schema Evolution:

  • Delta: Supports meta-data handling, allowing for schema changes without affecting the underlying data. This is managed through the transaction log.
  • Parquet: While it can handle schema evolution, changes might require rewriting or creating new files.

c. Time Travel:

  • Delta: Maintains versioned data, allowing users to query a snapshot of the data at any point in time.
  • Parquet: Lacks native versioning capabilities.

d. Storage Efficiency:

  • Delta: Implements mechanisms like Z-ordering (multi-dimensional clustering) and data skipping, optimizing both storage and query performance.
  • Parquet: Relies on columnar storage and compression for efficiency but lacks advanced optimizations like Z-ordering.

e. Merge, Update, and Delete:

  • Delta: Supports MERGE INTO operations, making it easier to handle scenarios like change data capture (CDC).
  • Parquet: These operations typically require reading the entire dataset, making changes, and then writing it back.

3. Performance Insights

  • Delta: Optimized for scenarios with frequent updates. The transaction log ensures minimal data movement, and features like Z-ordering can lead to faster query performance.
  • Parquet: While read-heavy workloads perform well with Parquet, write-heavy or update-heavy workloads might face challenges due to the lack of transactional support.

4. Compatibility and Ecosystem

  • Delta: Built on top of Spark, it offers seamless integration with the Spark ecosystem. Moreover, Delta tables can be read by any tool that supports Parquet.
  • Parquet: Being a widely adopted format, it boasts broad compatibility across the Hadoop ecosystem, including tools like Hive, Impala, and Presto.

5. The Verdict

While Parquet is a robust columnar storage format that has served the big data community well, Delta brings in features that cater to the evolving needs of data engineering and data science teams. Its emphasis on transactional integrity, combined with advanced optimizations, positions Delta as a formidable player in the big data storage arena.

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