Delta Lake offers several advantages over Hive. One of the key benefits is its design for petabyte-scale data lakes with streaming and fast access at the forefront. This makes it more suitable for near-real-time streams, unlike Hive. Delta Lake also handles small files more efficiently and works well with Python, which can be a challenge with Hive.
Delta Lake's metadata is stored in atomic, monotonically increasing JSON and Parquet based snapshots in the _delta_log/ directory, allowing for fast, distributed metadata processing with Spark. This is a significant advantage over Hive, especially for large-scale data processing tasks.
In terms of when to use Delta Lake for data processing tasks, it is particularly beneficial when dealing with large-scale data lakes and streaming data. It's also a good choice when you need to share data in an open way leveraging Delta Sharing. Delta Lake is also recommended when you need to perform complete and incremental updates to existing tables, compact files, restore previous table versions, and perform garbage collection of tables in the Lakehouse.
