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# Unify your productivity and AI data with Lakeflow Connect

**[databricks](https://daily.dev/sources/databricks)** · 8 min read · 0 upvotes · 0 comments

## Summary

Databricks' Lakeflow Connect offers managed connectors for productivity tools like wikis, project trackers, email, and AI foundation model providers, landing data as governed Delta tables in Unity Catalog. Customers cite concrete results: Orizon brought over one million Jira tickets from 168 projects into Databricks with 100% daily sync success, and M3 Accounting says its GitHub connector saves about an hour a week. The connectors also ingest unstructured content like Confluence pages, SharePoint files, and email messages for grounding AI agents and Genie dashboards. Databricks offers Lakeflow Connect on a free tier.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.databricks.com/blog/unify-your-productivity-and-ai-data-lakeflow-connect>

## Questions this post answers

### How does Databricks Lakeflow Connect ingest data from Confluence and SharePoint for AI agents?

Lakeflow Connect provides fully managed native connectors that land unstructured content as governed tables in Unity Catalog. For Confluence, page content lands in the 'full_body_content' column as HTML or plain text, with attachments stored separately; files from sources like SharePoint land with a 'content' column as BINARY or FILE data, ready for parsing and indexing for retrieval.

_Developers building AI knowledge assistants can track Databricks ingestion tooling updates like this on daily.dev._

### What results have companies seen using Databricks Lakeflow Connect for Jira data?

Orizon, a Brazilian healthcare technology company, brought over one million tickets from 168 Jira projects into Databricks with daily synchronization and 100% success, then built AI/BI dashboards for its Steering Committee. Toyota Finance Australia also reported a zero-maintenance pipeline that automatically handles API complexity and schema changes for its Jira data.

_Teams evaluating managed Jira ingestion pipelines can follow real-world case studies like this on daily.dev._

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---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#data-engineering](https://daily.dev/tags/data-engineering), [#rag](https://daily.dev/tags/rag), [#databricks](https://daily.dev/tags/databricks), [#unity-catalog](https://daily.dev/tags/unity-catalog)

[View this post on daily.dev](https://daily.dev/posts/unify-your-productivity-and-ai-data-with-lakeflow-connect-tga8fput1)

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