Microsoft Fabric and Apache Iceberg

Microsoft Fabric and Apache Iceberg

Microsoft Fabric is Microsoft’s unified analytics platform that integrates data engineering, data warehousing, real-time analytics, and AI into a single SaaS service. Fabric supports Apache Iceberg through its OneLake storage layer — the universal, multi-cloud data lake that underpins all Fabric workloads.

Microsoft Fabric’s Iceberg integration enables bi-directional interoperability: Fabric can read Iceberg tables written by external engines (Spark, Dremio, Flink), and external Iceberg-compatible engines can read Fabric tables exposed via the Iceberg REST Catalog API.

OneLake: The Fabric Storage Foundation

OneLake is Fabric’s centralized, multi-cloud data lake storage built on Azure Data Lake Storage Gen2. All Fabric experiences (Lakehouse, Data Warehouse, Real-Time Analytics) store their data in OneLake — and OneLake supports multiple open table formats including Delta Lake (native) and Apache Iceberg (via integration).

OneLake uses the concept of a lake-centric storage model: one lake per Fabric tenant, organized into workspaces and items.

Fabric Lakehouse and Iceberg

Fabric’s Lakehouse experience uses Delta Lake as its native format internally. However, Fabric supports exposing Lakehouse tables as Iceberg via:

Iceberg API Endpoint

Fabric exposes an Iceberg REST Catalog endpoint for OneLake data:

Endpoint: https://api.fabric.microsoft.com/v1/workspaces/{workspace-id}/lakehouses/{lakehouse-id}/tables/iceberg

Authentication: Azure AD (Entra ID) bearer token

External engines connect via the REST Catalog:

# PyIceberg: connect to Fabric's Iceberg API
from pyiceberg.catalog import load_catalog

catalog = load_catalog(
    "fabric",
    **{
        "type": "rest",
        "uri": "https://api.fabric.microsoft.com/v1/workspaces/abc-123/lakehouses/def-456/tables/iceberg",
        "token": "<azure-entra-id-token>",
    }
)

# List and access tables
for table_id in catalog.list_tables("default"):
    print(table_id)

Reading External Iceberg in Fabric

Fabric Lakehouse can read externally-written Iceberg tables (Spark, Dremio, Flink) stored in Azure Data Lake Storage:

1. Mount the ADLS container in Fabric as an external data source.
2. Create a Fabric shortcut to the Iceberg table location.
3. Fabric reads the Iceberg metadata and presents the table in the Lakehouse UI.

Fabric Mirrored Databases

Mirroring in Fabric creates real-time replicas of operational databases (Azure SQL, PostgreSQL, MongoDB) as Delta/Iceberg tables in OneLake. Mirrored data becomes accessible to Iceberg-compatible engines via the Iceberg API endpoint.

This pattern is powerful for operational analytics: production database → Fabric mirroring → OneLake (Iceberg) → Dremio/Trino analytics.

Azure + Iceberg + Dremio Architecture

A common Azure Fabric + Dremio pattern:

Fabric Lakehouse (OneLake/ADLS)
  ├── Mirrored operational data (via Fabric mirroring)
  ├── Spark-processed Iceberg tables
  └── External Iceberg tables (written by Flink/Spark)

Dremio (connected to ADLS + Fabric Iceberg API)
  ├── Federates across Fabric Lakehouse + other sources
  ├── AI Semantic Layer over Fabric Iceberg tables
  └── AI Agent for natural language analytics

Fabric vs. Direct Azure + Iceberg

For organizations already committed to the Microsoft Azure ecosystem, Fabric provides a convenient Iceberg integration path. For multi-cloud or open ecosystem requirements, a direct Apache Iceberg deployment with Apache Polaris and Dremio provides more control and broader interoperability.