Delta vs. Parquet: A Deep Dive into Big Data Storage Solutions
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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.
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, Hive, Presto, 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…
