Data platforms for AI
The data layer that enterprise AI depends on, researched October 2026: platforms and lakehouses, open table formats and catalogs, transformation and semantic layers, ingestion and orchestration, governance and quality, and ontology layers. Company notes for the vendors are in COMPANIES. Related BI notes: bi-tools-landscape-2026; AI-platform view: databricks-ai-bi, snowflake-intelligence, microsoft-fabric.
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Data and AI platforms
- Databricks Data Intelligence Platform
- Snowflake AI Data Cloud
- Microsoft OneLake and Fabric as a Data Platform
- Google BigQuery as an AI data platform
- AWS Data and AI Platform
- Oracle AI Data Platform and Autonomous AI Database
- Teradata Vantage and VantageCloud
- Cloudera Data Platform
- ClickHouse
- SAP Business Data Cloud (BDC)
- Palantir Foundry and AIP
Open formats, catalogs and engines
- Apache Iceberg
- Delta Lake
- Apache Hudi and Apache Paimon
- Open Lakehouse Catalogs
- Trino and Starburst
- Dremio
- DuckDB and MotherDuck
- Postgres for AI Agents
Transformation and semantic layers
- dbt (data build tool)
- dbt learning path
- SQLMesh and Tobiko Data
- Coalesce (data operating layer)
- Cube (semantic layer)
- AtScale
- Open Semantic Interchange (Apache Ossie)
- Semantic layers compared (October 2026)
Ingestion, streaming and orchestration
- Data pipelines for AI
- Fivetran
- Airbyte
- Matillion
- Confluent and Apache Kafka
- Informatica Intelligent Data Management Cloud (IDMC)
- Talend Data Fabric and Qlik Talend Cloud
- Apache Airflow
- Dagster
- Prefect
Catalogs, governance and quality
- Data catalogs and governance platforms compared (2026)
- Microsoft Purview
- Databricks Unity Catalog
- DataHub
- OpenMetadata
- Monte Carlo (data and AI observability)
- Soda (data quality and data contracts)
- Great Expectations (GX)
- Ataccama ONE
Ontology and knowledge graphs