Unify your productivity and AI data with Lakeflow Connect

Unify your productivity and AI data with Lakeflow Connect | Databricks Blog Skip to main content

Summary

Productivity and collaboration tools hold your most valuable enterprise knowledge, but this data has been historically siloed across dozens of separate applications, behind a custom API integration for every source.

Lakeflow Connect provides native, fully managed connectors for Atlassian (Jira, Confluence), Microsoft (SharePoint), Google Workspace (Drive, Outlook, Gmail), GitHub, and major AI providers (OpenAI, Anthropic) with unified governance and no infrastructure to manage.

Since both unstructured and structured data lands directly in Unity Catalog, data teams can easily power enterprise knowledge bases, AI agents and developer productivity dashboards without building or maintaining custom data pipelines.

This is the second post in our series on how Lakeflow Connect brings fully managed data ingestion to every line of business. Following our deep dive into unifying marketing data , this post focuses on unlocking the enterprise knowledge trapped within your organization's productivity stack. We’ll explore how native connectors for wikis, project management tools, and AI foundation models give your downstream AI agents the complete business context they need. One platform, any source Lakeflow Connect provides a comprehensive suite of fully managed native connectors across SaaS applications, databases, and file sources that ingest your data directly into the Databricks Platform . Each connector can be set up via a point-and-click UI or a simple API. From there, your data is incrementally ingested as governed, managed tables in Unity Catalog , with no infrastructure for you to provision, manage, or scale. Prioritized by customer demand, Lakeflow Connect's ingestion connectors span every line of business, including Customer 360, Operations, Finance, and Productivity—the stack closest to day-to-day work. One platform, every productivity tool The average company's productivity stack is a sprawl of project trackers, wikis, document stores, inboxes, and AI tools, each holding only a slice of how work gets done. Getting that data out has historically required a patchwork of custom pipelines that are slow to build and painful to maintain. The bar is high enough that the most impactful use cases stay out of reach, for example: AI agents powered by your enterprise's internal knowledge or visibility into operational analytics. Lakeflow Connect resolves this by providing built-in connectors to ingest seamlessly from across the productivity stack: COLLABORATION : How does work get tracked and shipped? Jira , Monday.com , Smartsheet , Aha! , and GitHub ENTERPRISE KNOWLEDGE : What collective expertise does your company run on? Confluence , Notion , Glean , SharePoint , and Google Drive EMAIL : How do your teams communicate? Outlook and Gmail AI : How is your organization adopting AI? OpenAI and Anthropic

With this breadth of connectors, Lakeflow Connect becomes the ingestion engine that brings scattered productivity data into one governed foundation, without the integration bottlenecks that traditionally slow teams down. For example, Toyota Finance Australia , the financing arm of one of the world's largest automakers, uses Lakeflow Connect to bring its Jira data into Databricks as a fully managed, zero-maintenance pipeline. “Lakeflow Connect for Jira has transformed how we onboard and consume Jira data within our enterprise data management platform. Moving to the fully managed, UI-driven experience offered by Databricks provided a seamless, zero-maintenance pipeline that automatically handles API complexity and schema changes while delivering governed, analytics-ready Delta tables in the Databricks Lakehouse with minimal effort and maximum reliability.” —Charbel Khoury, Head of Data Office, Toyota Finance Australia Limited Another customer, VaxCare , a vaccine management company runs the largest primary-care vaccination network in the U.S., and uses Lakeflow Connect's Confluence connector to feed its internal knowledge base into Databricks and ground an AI agent in it. "Lakeflow Connect's Confluence connector gives us a seamless way to feed our internal knowledge base into Databricks, making that data searchable and actionable for our chatbot today—and ready to power future AI solutions as we build them."  —Yasaman Malone, VP of Optimization and AI, VaxCare Unstructured content, AI-ready out of the box AI agents can provide instant, grounded answers from your company's collective knowledge without employees having to hunt across wikis, drives, and inboxes. But an agent is only as good as the data behind it, and most of that is unstructured. With Lakeflow Connect, this is not a problem; our connectors can land unstructured content like files, pages, and messages as governed tables in Unity Catalog. The exact data, and the format it arrives in, varies by source: SharePoint : The SharePoint connector brings in the unstructured files themselves (PDFs, Word documents, slide decks, and images). Each unstructured file lands as one row, with its raw bytes stored in a content column (either as BINARY or FILE type), with per-file metadata stored alongside. Structured files (Excel files, CSVs, etc.) can also be landed directly as Delta tables. Google Drive : The Google Drive connector works the same way for unstructured files, landing each document’s raw bytes as one row in a content column. Structured files (Google Sheets, CSVs, etc.) can also be landed directly as Delta tables. Confluence : The Confluence connector brings in the content of every page and blog post as STRUCT type in a body field of the pages and blogposts tables. Outlook : The Outlook connector stores the email body in a full_body_content column as a text string (either HTML or plain text), and it stores the attachments in an attachments column as BINARY type. Both live in the email_messages table. Gmail : The Gmail connector brings in the full message body as a text string, held in the messages table. It also ingests a reference to each attachment as an attachmentId . GitHub : The GitHub connector brings in repository text files (source code, Markdown, and config), stored in the content column of the repo_contents table.

Whatever the source, the content lands in Unity Catalog as a governed table, where you can begin to transform it for downstream AI use cases. Text that comes in...