Apache Iceberg vs Databricks
No clear leader: Apache Iceberg (62.2) and Databricks (59.8) are within the 5-point margin; treat as a tie. The attribute-by-attribute breakdown below, with a source and date on every value, is the honest way to compare them.
Capabilities
Feature-by-feature on the axes that matter for data warehouse software. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
Apache Iceberg
Apache Iceberg is a table format that enables reliable, ACID-compliant SQL operations on big data. It allows multiple query engines—such as Spark, Trino, Flink, Hive, and Impala—to safely read and write the same tables concurrently, bringing traditional database semantics to analytics workloads.
Databricks
Databricks is a unified data, analytics, and AI platform for enterprises that combines data warehousing, machine learning, and AI agent capabilities through its lakehouse architecture and serverless Postgres offering (Lakebase).
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Databricks
Pay-as-you-go usage-based pricing from $0.069 / DBU or CU per second. Committed Use Contracts available for volume discounts. Free tier available.
- Data EngineeringFrom $0.15 / DBU per second
- Orchestrate data processing
- Build streaming and batch pipelines
- Ingest data from multiple sources
- Data WarehousingFrom $0.22 / DBU per second
- SQL analytics
- BI reporting
- Available in Classic and Serverless compute
- Interactive WorkloadsFrom $0.40 / DBU per second
- Data science workloads
- Build custom applications
- Security and governance
- Operational DatabaseFrom $0.069 / CU per second
- Postgres database
- Data and feature serving
- Artificial IntelligenceFrom $0.07 / DBU per second
- GenAI applications
- Model Serving
- AI Functions
- Model Training
- GenieFree
- Natural language Q&A
- AI Coding Assistant
- Enterprise Knowledge access
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
Apache Iceberg
- Spark
- Trino
- Presto
- Flink
- Hive
- Impala
- PyArrow
- DuckDB
- Polars
- Pandas
- Ray
- Datafusion
- BigQuery
- SQLAlchemy
- S3
- GCS
- Azure Data Lake Storage
- Hadoop
- Kerberos
- Thrift
Databricks
- Teams
- Slack
- Confluence
- Power Platform
- Copilot Studio
- Zerobus
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.