Delta Lake vs Apache Parquet: What are the differences?

Apache Parquet and Delta Lake are two popular data storage formats used in big data and data lake scenarios. Here are the key differences between Apache Parquet and Delta Lake:

1. Data Lake Features: Apache Parquet is a columnar storage format that focuses on efficient data compression and query performance. It provides excellent read performance, low storage footprint, and efficient column pruning, making it suitable for analytical workloads. Delta Lake, on the other hand, is an open-source data lake storage layer that adds transactional capabilities and reliability on top of existing data lakes. Delta Lake offers features like ACID transactions, schema evolution, time travel, and data versioning, enabling data governance, data quality management, and stream processing on data lakes.

2. Data Consistency and Reliability: Parquet is a file format that offers high performance but does not provide built-in mechanisms for data consistency or reliability. It does not handle issues like concurrent writes or data consistency guarantees out of the box. In contrast, Delta Lake addresses these challenges by providing ACID (Atomicity, Consistency, Isolation, Durability) transactions. Delta Lake ensures that data operations are atomic, consistent, and durable, providing reliable data updates and eliminating issues like data corruption or partial writes.

3. Streaming and Data Updates: Parquet is primarily designed for batch processing and does not provide built-in support for streaming or real-time data updates. It is optimized for read-heavy workloads and large-scale analytics. Delta Lake, on the other hand, supports both batch and streaming workloads. It enables the ingestion of streaming data with low-latency updates and allows for real-time data pipelines and continuous data processing. Delta Lake's transactional capabilities ensure data integrity and consistency, even in streaming scenarios.

4. Data Management and Schema Evolution: Parquet has a static schema that needs to be defined upfront and is typically used with external schema management tools. Any changes to the schema require coordination and updates to external metadata. Delta Lake, however, supports schema evolution and provides schema enforcement capabilities. It allows for schema evolution over time, enabling changes to the table schema without breaking downstream applications. Delta Lake also provides schema validation and metadata management to work with evolving data structures.

In summary, Apache Parquet is a performant columnar storage format optimized for read-heavy analytics workloads. It provides efficient data compression and query performance but lacks built-in transactional capabilities and data management features. Delta Lake, on the other hand, adds transactional capabilities and reliability to existing data lakes, enabling ACID transactions, data consistency, and schema evolution. It supports both batch and streaming workloads, making it suitable for real-time data processing and data governance in data lake environments.