Google BigQuery

Serverless Google Cloud data warehouse with managed Iceberg tables, automatic optimization, Storage Write API streaming, and deep GCP ecosystem integration

Key Features

80

Managed + External

Dual Table Model

BigQuery-managed Iceberg (internal catalog, full DML) and BigLake external Iceberg (Dataplex/HMS/Glue via GCS, query + limited writes)

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100

Zero Maintenance

Automatic Optimization

Fully automatic file-size tuning, clustering, metadata compaction & orphan-file GC. No user-issued OPTIMIZE or VACUUM commands required

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75

Managed Full, External Limited

Asymmetric DML Support

Managed tables: INSERT, UPDATE, DELETE, MERGE with GoogleSQL semantics. External tables: limited INSERT support via Dataflow/Spark

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95

Auto MoR + CoW

Intelligent Storage Strategy

Operations generate position/equality delete files (MoR). Automatic compaction, clustering, and garbage collection (CoW) in background

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70

High-Throughput Preview

Storage Write API Streaming

High-throughput streaming via Storage Write API (Preview) - Dataflow, Beam, Spark. No built-in CDC apply; use Datastream + Dataflow patterns

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40

Limited File Formats

Parquet-Only Format

Parquet only (preview). ORC/Avro not yet supported. v2 required for managed tables, v3 evaluation planned for 2025

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60

Managed vs External

Differential Time Travel

Managed tables: FOR SYSTEM_TIME AS OF syntax translating to snapshots. External BigLake tables: no SQL time travel currently

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95

IAM + Column Masking

BigQuery-Native Security

IAM permissions like native BigQuery tables. Column-level security & masking on managed Iceberg. External via BigLake/Dataplex policy tags

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50

Pre-GA Features

Preview Status Limitations

Feature is Pre-GA (behavior may change). No table rename/clone, limited concurrency, external writes rely on Dataflow/Spark

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100

Native Services

GCP Ecosystem Integration

BigQuery ML on Iceberg data, Dataform transformations, BigQuery Omni cross-cloud queries, end-to-end lineage through Dataplex

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Google BigQuery Iceberg Feature Matrix

Comprehensive breakdown of Iceberg capabilities in Google BigQuery

Dimension	Support Level	Implementation Details	Status
Catalog Types	PartialDual Model	BigQuery-managed (internal) + BigLake external (Dataplex/HMS/Glue); no REST/Nessie	Preview
SQL Analytics	PartialTable-dependent	Managed: full CREATE/CTAS/INSERT/DML; External: SELECT + limited INSERT via Dataflow	Preview
DML Operations	PartialManaged Full	Managed: INSERT/UPDATE/DELETE/MERGE; External: limited INSERT via external tools	Preview
Storage Strategy	FullAuto Optimization	MoR operations + automatic CoW optimization; background compaction/clustering/GC	Preview
Streaming Support	PartialStorage Write API	High-throughput via Storage Write API; Dataflow/Beam/Spark; no built-in CDC	Preview
Format Support	LimitedParquet Only	Parquet only; no ORC/Avro; v2 required; v3 evaluation planned 2025	Preview
Time Travel	PartialManaged Only	Managed: FOR SYSTEM_TIME AS OF; External: no SQL time travel currently	Preview
Schema Evolution	FullMetadata-only	ADD/DROP/RENAME columns; type widening; instant reflection in information_schema	Preview
Security & Governance	FullIAM + Column Masking	BigQuery IAM + column-level security/masking; BigLake/Dataplex policy tags	Preview
GCP Integration	FullNative Ecosystem	BigQuery ML, Dataform, Omni, Dataplex lineage; multi-engine access to managed tables	Preview
Automatic Optimization	FullZero Maintenance	Automatic file compaction, clustering, metadata optimization, garbage collection	Preview
Preview Limitations	SeveralPre-GA	No table rename/clone; concurrency limits; external DML limited; behavior may change	Preview

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Use Cases

Serverless Data Warehouse

Fully managed Iceberg tables with automatic optimization

• Modern data warehouse with zero maintenance overhead
• Analytics workloads requiring automatic optimization
• Teams wanting BigQuery's serverless benefits on Iceberg
• High-frequency update scenarios with background optimization

GCP-Native Analytics Platform

Deep integration with Google Cloud ecosystem services

• BigQuery ML on Iceberg data for machine learning
• Dataform transformations on Iceberg tables
• Cross-cloud analytics with BigQuery Omni
• End-to-end data lineage through Dataplex

Streaming Analytics with Storage Write API

High-throughput streaming ingestion for real-time analytics

• Real-time streaming data analysis
• High-volume ingestion via Dataflow/Beam/Spark
• Near real-time dashboard and reporting
• CDC processing with Datastream integration

Multi-Engine Data Lake

Iceberg tables accessible from multiple GCP services

• Data shared between BigQuery and Dataproc Spark/Flink
• Multi-engine analytical workloads
• Hybrid batch and streaming architectures
• Open format data lake with BigQuery performance

Resources & Documentation

Official Documentation

Complete API reference and guides

Getting Started Guide

Quick start tutorials and examples

BigQuery Iceberg Tables Documentation

Documentation

Announcing BigQuery Iceberg Support

Documentation

Create External Iceberg Tables

Documentation

DML Operations Guide

Documentation

Column-level Security

Documentation

Time Travel Documentation

Documentation

BigQuery Iceberg Limitations

Documentation

Metadata Caching for External Tables

Documentation