What Is Delta Lake (www.ibm.com)

Delta Lake is an open table format (developed by Databricks, open‑sourced in 2019) that adds a transaction log and metadata layer on top of Parquet files to turn raw data lakes into reliable, queryable table storage. The transaction log records file paths, column stats (min/max), schema, and every change, enabling ACID transactions, schema enforcement/evolution, time travel (versioned rollbacks) and what is effectively mutability for immutable Parquet files by changing metadata rather than rewriting content. Those features let engines skip irrelevant files (predicate/file pruning), leverage layout optimizations like Z‑ordering, and run SQL, batch and streaming workloads directly on the lake. For the AI/ML community, Delta Lake matters because it raises data reliability and accessibility at scale: schema checks and ACID guarantees reduce corrupted or partial training datasets, time travel helps reproducible experiments, and unified streaming+batch support simplifies feature pipelines. Delta integrates with Spark, Hive, Flink, Trino and offers APIs (Python/Java/Scala), and competes with other open table formats—Iceberg (Parquet/ORC/Avro, multi‑tier metadata) and Hudi (incremental/CDC focus). Its role in the lakehouse architecture makes it a practical backbone for production ML pipelines; upcoming releases (Delta Lake 4.0) promise further capabilities.