# apache/iceberg

Apache Iceberg

Repository: https://github.com/apache/iceberg
Canonical: https://ross.abutalabs.com/products/iceberg
Homepage: https://iceberg.apache.org/
Language: Java
License: Apache-2.0
License Family: permissive
Topics: iceberg, apache, hacktoberfest
Last push: 2026-08-26T15:23:08+00:00

## Health v2 (maintenance only)
Score: 90/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 72, longevity 100
- inputs: {"age_days": 2844, "days_push": 7, "days_rel": 105, "gap_med": 39, "n_releases_24m": 12}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9177, forks 3479 (observed 2026-08-28T04:10:28.716517+00:00)

## What it is
Apache Iceberg is a high-performance open table format for huge analytic datasets, bringing SQL table reliability to big data lakes. This repository hosts the core Java reference implementation and integrations for engines like Spark, Flink, Trino, Presto, and Hive.

## Use cases
- manage petabyte-scale tables on a data lake
- run ACID transactions and concurrent writes from Spark, Flink, and Trino on the same tables
- perform schema evolution without rewriting data files
- query historical table snapshots with time travel and rollback
- implement hidden partitioning and data compaction for faster queries
- build a lakehouse with MERGE INTO, updates, and deletes

## When to choose
- you need a reliable, engine-agnostic table format for a data lake or lakehouse
- multiple processing engines must safely read and write the same tables concurrently
- you need schema/partition evolution, time travel, or rollback on large analytic tables

## When to avoid
- you need a small-scale operational OLTP database rather than analytic tables
- your stack cannot run JVM-based engines or use the Iceberg libraries
- you only need simple single-engine storage without table format features

## Facets
- artifact type: library
- maturity: stable
- function: database, serialization, data-science, etl, streaming
- domain: databases, big-data, analytics, data-science
- platform: jvm, cross-platform
- tags: table-format, lakehouse, parquet, schema-evolution, time-travel, spark, flink, trino, data-lake, data-engineering

## Member repositories
- apache/iceberg (main) score 90

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:28.716517+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:23:22.136884+00:00, confidence not recorded.
  - readme: https://github.com/apache/iceberg (fetched 2026-08-28T04:10:28.716517+00:00, sha 0ef7155e733e)
  - homepage: https://iceberg.apache.org/ (fetched 2026-08-29T08:23:37.746138+00:00, sha 28e5f9666cc3)
  - site_page: https://iceberg.apache.org/spark-quickstart (fetched 2026-08-29T08:23:37.749219+00:00, sha 725e700af40e)
  - site_page: https://iceberg.apache.org/docs/latest (fetched 2026-08-29T08:23:37.751120+00:00, sha ca59b0490785)
  - site_page: https://iceberg.apache.org/docs/latest/spark-writes (fetched 2026-08-29T08:23:37.754386+00:00, sha fdd0f08cfb50)
  - site_page: https://iceberg.apache.org/docs/latest/evolution (fetched 2026-08-29T08:23:37.756135+00:00, sha 3e205eab2610)
  - site_page: https://iceberg.apache.org/docs/latest/partitioning (fetched 2026-08-29T08:23:37.757818+00:00, sha fa27a0672747)
  - site_page: https://iceberg.apache.org/docs/latest/spark-queries (fetched 2026-08-29T08:23:37.759412+00:00, sha 214c981524ad)
  - site_page: https://iceberg.apache.org/flink-quickstart (fetched 2026-08-29T08:23:37.761166+00:00, sha 46b19e1a6d00)
  - site_page: https://iceberg.apache.org/releases (fetched 2026-08-29T08:23:37.752849+00:00, sha 2334f55ef258)
- Data as of 2026-08-30T08:39:29.467469+00:00.