> ## Documentation Index
> Fetch the complete documentation index at: https://docs.streamnative.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview - Lakehouse Tables

**StreamNative Lakehouse Tables**

StreamNative Lakehouse Tables provide a unified way to expose streaming topics as open table format objects—such as Apache Iceberg and Delta Lake—directly within StreamNative Cloud. With Lakehouse Tables, data produced to Pulsar or Kafka-compatible topics can be automatically stored in object storage in a transactional, analytics-ready table format. This enables seamless integration between real-time data streams and downstream analytics, AI/ML, and governance systems.

**Overview**

StreamNative Lakehouse Tables bridge the gap between streaming and batch systems by converting message data from topics into table-backed datasets. Each Lakehouse Table maintains:

* Metadata (schema, manifest lists, snapshots)
* Data files (Parquet/columnar format)
* Transaction logs (for table evolution)

These components reside in user-controlled object storage, ensuring low cost, high durability, and interoperability with a broad ecosystem of tools.

**Key Capabilities**

**1. Open Table Format Support**

StreamNative Lakehouse Tables support industry-standard formats:

* Apache Iceberg
* Delta Lake (Delta 2.0 and above)

This ensures compatibility with engines such as Databricks, Snowflake, Spark, Trino, Flink, BigQuery, StarTree, and more.

**2. Native Topic-to-Table Mapping**

Each table is backed by one or more StreamNative topics. Data is automatically:

* Ingested from the streaming topic
* Serialized into Parquet files
* Committed into the table as immutable snapshots
* Made available for SQL queries and analytical engines

This provides a streaming-first lakehouse architecture without external ingestion pipelines.

**3. Object Storage as the Source of Truth**

All table artifacts are stored in object storage such as:

* Amazon S3
* Google Cloud Storage
* Azure Blob Storage

This enables low-cost storage, independent scaling, and easy interoperability.

**4. Schema-Aware and Schema-Safe**

Lakehouse Tables use StreamNative’s schema registry to ensure:

* Schema inference from topics
* Backward/forward compatible evolution
* Safe writes with schema enforcement
* Automatic mapping to Iceberg/Delta schemas
* Users retain full control over table evolution policies.

**5. Transactional Guarantees**

Using open table format guarantees, Lakehouse Tables support:

* ACID transactions
* Snapshot isolation
* Time travel (via historical snapshots)
* Incremental reads

This brings reliability and consistency to streaming data workflows.

6. Full Interoperability with Data and AI Platforms

Once a table is materialized in Iceberg or Delta Lake format, it is fully queryable by:

* Databricks
* Snowflake
* BigQuery Managed Tables
* Apache Spark, Flink, and Trino
* StarTree and Pinot
* DuckDB
* pandas & PyArrow

No connectors or intermediate ETL is required.