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DirectoryData & AnalyticsData Warehouse & LakehouseApache Iceberg

Apache Iceberg

Open Source

About Apache Iceberg

Apache Iceberg is an open table format for analytic datasets. It is described as a high-performance format for large analytic tables. It brings SQL table reliability and simplicity to big data. It allows multiple engines to work with the same tables simultaneously.

Used for

  • Merging new data, updating existing rows, and performing targeted deletes using SQL commands.
  • Running time-travel queries using a table snapshot.
  • Rolling back tables to a previous version.

From Apache Iceberg's own website, read 2026-10-23. How we build listings.

How to evaluate Data Warehouse & Lakehouse

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Apache Iceberg. The category covers two kinds of product, so both frameworks are here. Most buyers need one of them.

Cloud Data Warehouse
25%
Query Performance & Concurrency
TPC-DS benchmarks, concurrent user support, query queuing, automatic scaling, sub-second response for dashboards
20%
Data Ingestion & Integration
Streaming ingestion (Kafka, Kinesis), batch loading, CDC support, native connectors, data sharing/marketplace
20%
Governance & Security
Column/row-level security, dynamic data masking, data lineage, access policies, audit logging, compliance certifications
15%
AI/ML Integration
Native ML runtimes (Python/Spark), feature store, vector search, LLM integration, model serving
10%
Cost Management
Compute/storage separation, auto-suspend, resource monitors, usage attribution, reserved capacity pricing
10%
Ecosystem & Tooling
BI tool compatibility, dbt/Airflow integration, Iceberg/Delta support, partner ecosystem, marketplace
Data Lakehouse Platforms
25%
Open Format & Catalog Interoperability
Native read AND write to Apache Iceberg and/or Delta Lake, Iceberg REST Catalog support, credential vending for external engines, cross-format bridges (Delta UniForm, Apache XTable), and whether grants and lineage travel with the table when another engine reads it
20%
Query Engine Performance & Concurrency
Vectorized execution (Photon, Arrow/Gandiva, native engines), caching and materialization (reflections, result cache, Warp Speed), high-concurrency BI behavior, autoscaling, and predictable performance on your own data — not vendor benchmarks
20%
Data Engineering, Streaming & AI/ML
Batch and streaming ingestion into open tables, incremental/CDC and upsert support, orchestration, ML lifecycle (feature store, training, model serving), notebook and Python/Spark depth, and native LLM/agentic and vector capabilities
15%
Governance, Security & Lineage
A unified catalog spanning tables, files, ML models and (increasingly) unstructured data; fine-grained RBAC/ABAC, row/column masking, data sharing, automated lineage, and consistent policy enforcement across every engine that touches the data
10%
Operational Simplicity & Table Maintenance
Automated compaction, clustering, snapshot expiry and orphan-file cleanup; serverless vs. cluster sizing; multi-cloud and hybrid/on-prem reach; admin and FinOps tooling; and how much table toil the team must own versus the platform absorbing it
10%
Cost Model & Consumption Control
Consumption unit (DBU, credit, capacity unit, bytes/slots), separation of storage and compute, idle-suspend and autoscaling guardrails, egress and cross-region exposure, workload isolation, and the FinOps tooling to attribute and cap spend

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Quick Facts

iceberg.apache.org
CategoryData & Analytics
SubcategoryData Warehouse & Lakehouse

We publish a detail only when we can point at the page it came from. Know when Apache Iceberg was founded or where it is based? Send it to us, free.

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