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# Building live intelligence: Databricks AI Search supports Streaming Tables, Materialized Views, and Managed tables using Iceberg

**[databricks](https://daily.dev/sources/databricks)** · 3 min read · 0 upvotes · 0 comments

## Summary

Databricks AI Search now connects directly to Streaming Tables, Materialized Views, and Managed Iceberg v3+ tables, not just Delta tables. This removes the need to maintain a separate synchronized Delta copy of these sources for search indexing. Streaming Tables suit fraud detection and real-time IoT analytics, Materialized Views enable session-aware and personalized search, and Managed Iceberg tables let search work across open-format data without migration. Indexing uses the same create_delta_sync_index workflow as before, with a CONTINUOUS pipeline type shown for a live transactions use case.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.databricks.com/blog/building-live-intelligence-databricks-ai-search-supports-streaming-tables-materialized-views>

## Questions this post answers

### Can Databricks AI Search index a Streaming Table directly without converting it to Delta first?

Yes, Databricks AI Search natively supports Streaming Tables, Materialized Views, and Managed tables using Iceberg v3+ in addition to Delta tables. Previously customers had to create a separate synchronized Delta representation of these sources; now AI Search connects to them directly, using the same create_delta_sync_index workflow with a CONTINUOUS pipeline type.

_Evaluating real-time search architectures on Databricks is easier when you track platform changes like this on daily.dev._

### What is Materialized Views used for in Databricks AI Search?

Materialized Views aggregate, filter, and join data from multiple sources into customized state that AI Search can index directly. They support personalized and session-aware search experiences, such as recommendations based on recent user activity or tailoring results to evolving preferences, with the view incrementally maintained as source data changes.

_Teams designing personalization features can follow Databricks search updates like this on daily.dev._

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---

Tags: [#vector-search](https://daily.dev/tags/vector-search), [#databricks](https://daily.dev/tags/databricks), [#apache-iceberg](https://daily.dev/tags/apache-iceberg)

[View this post on daily.dev](https://daily.dev/posts/building-live-intelligence-databricks-ai-search-supports-streaming-tables-materialized-views-and--k0oniu2an)

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