Apache Iceberg vs Delta Lake vs Hudi

Apache Iceberg, Delta Lake and Apache Hudi are open table formats: layers of metadata over ordinary data files, usually Parquet, that turn a folder of files into a table with transactions. They solve the same core problems and increasingly share features, so the differences lie in design emphasis and ecosystem. This guide explains what they have in common, where each stands out, and how to choose.

What all three solve

A raw data lake is a directory of files. Without a table format, a job that fails halfway leaves partial output, two writers can overwrite each other, readers can see a mixture of old and new files, updating one row means rewriting files by hand, and nobody can reproduce yesterday's version of a table. All three formats address this with metadata that records which files make up each version of the table:

• Atomic commits so a write is all or nothing.
• Isolation so readers see a consistent snapshot while writers work.
• Row-level changes: update, delete and merge.
• Time travel to read or restore earlier versions.
• Schema evolution under control, rather than whatever files happen to contain.

Delta Lake explained shows these working on a real table, and all three store data in the columnar formats compared in Parquet vs ORC vs Avro.

Apache Iceberg: engine-neutral and partition-smart

Iceberg describes itself as a table format for data lakes, with schema evolution, hidden partitioning, partition layout evolution, time travel and rollback, and atomic changes under serializable isolation (Iceberg docs). Its standout feature is hidden partitioning. In Hive-style tables, a partition such as event_date is an explicit column: writers must fill it correctly and readers must remember to filter on it, or queries scan everything. Iceberg instead derives partition values from a transform, such as day(event_time), keeps track of the relationship, and uses it automatically when a query filters on event_time (Iceberg docs: partitioning). Because queries no longer depend on the physical layout, a table can change its partitioning as data grows, from daily to hourly for example, without rewriting history or breaking queries.

Iceberg's other strength is breadth of engine support: the documentation lists Spark, Flink, Hive, Trino, Presto, DuckDB, BigQuery, Snowflake, Athena and more. That makes it a common choice when many different engines must read and write the same tables.

Delta Lake: the transaction log and the Spark ecosystem

Delta Lake provides ACID transactions with serializable isolation, schema enforcement, time travel and merge, update and delete (Delta Lake docs), implemented through an ordered log of JSON commits beside the data. It grew up with Apache Spark and is the native table format of Databricks, where it is most deeply integrated, though it is open source and readable by other engines and libraries. For teams already on Spark or Databricks, Delta is usually the path of least resistance; the Databricks tutorial introduces that platform.

Delta also offers a bridge between formats. UniForm generates Iceberg and Hudi metadata for a Delta table over the same Parquet files, so Iceberg and Hudi clients can read it (Delta docs: UniForm). The limits matter: those clients can only read, and UniForm does not work on tables with deletion vectors enabled.

Apache Hudi: built for upserts and incremental pipelines

Hudi calls itself an open data lakehouse platform, emphasising efficient upserts and deletes, indexes, and built-in ingestion, clustering and compaction services (Hudi docs: overview). Its distinctive choice is between two table types (Hudi docs: table types):

Hudi also supports incremental queries, which return only data written since a given point on the table's timeline. That lets downstream jobs process just what changed, the pattern behind change data capture pipelines; CDC explained covers the source side.

Side by side

The formats borrow from each other quickly, so treat any feature table, including this one, as a starting point and check current documentation for the versions you would run.

How to choose

• Follow your platform first. Databricks points to Delta; engine-neutral lakes queried by Trino, Snowflake, BigQuery or Athena often point to Iceberg; pipelines built around frequent upserts may favour Hudi.
• List the engines that must write, not just read. Read access across formats is increasingly available; multi-engine writes are where format choice bites.
• Test your hardest workload: a large merge, a late-data backfill, a partition change.
• Plan maintenance: every format needs compaction of small files and cleanup of old snapshots, and those jobs cost compute.

Whichever you pick, the principles in Apache Spark explained and good schema evolution practice apply unchanged.

Data lakes, data warehousing, Apache Spark and a data lake implementation project are part of the Data Engineering course.

Sources

• Apache Iceberg documentation — Apache Iceberg
• Partitioning (Apache Iceberg documentation) — Apache Iceberg
• Introduction to Delta Lake (Delta Lake documentation) — Delta Lake
• Universal Format (UniForm) (Delta Lake documentation) — Delta Lake
• Apache Hudi overview (Apache Hudi documentation) — Apache Hudi
• Table and query types (Apache Hudi documentation) — Apache Hudi