Using an innovative new table design, Delta supports both batch and streaming use cases with high query performance and strong data reliability while requiring a simpler data pipeline architecture:
Increased query performance – Able to deliver 10 to 100 times faster performance than Apache Spark(™) on Parquet through the use of key enablers such as compaction, flexible indexing, multi-dimensional clustering and data caching.
Improved data reliability – By employing ACID (“all or nothing”) transactions, schema validation / enforcement, exactly once semantics, snapshot isolation and support for UPSERTS and DELETES.
Reduced system complexity – Through the unification of batch and streaming in a common pipeline architecture – being able to operate on the same table also means a shorter time from data ingest to query result. Schema evolution provides the ability to infer schema from input data making it easier to deal with changing business needs.
The Azure version of Databricks is quickly reaching parity with the classic AWS-hosed version.