How to get your Databricks data ready for agentic AI

Databricks holds the data behind your advanced analytics — product usage, customer history, model inputs and outputs, and the prepared data sets your data science teams built. Getting that data ready for agentic AI means giving AI agents access to a centralized, cleansed, and governed version of it, so they answer questions like "which customers does our churn model flag, and what do they have in common?" in seconds rather than days. Getting your Databricks data AI agent-ready means moving it into the warehouse or data lake where your agents run, keeping it current, and modeling it so agents work from trusted definitions and join it with data from your other systems. Fivetran + dbt Labs delivers the data foundation for agentic AI: Fivetran moves your Databricks data reliably, and dbt transforms it into trusted, AI-ready tables.

Why Databricks data is critical for agentic AI

Databricks is where your data science and engineering teams do their most advanced work. The results of that work, from refined data sets to model outputs, are valuable well beyond those teams.

That value stays narrow. A product leader wants to see what a churn model found, and waits for a data scientist to export it. A head of data hears from 3 departments who each need the same prepared data in their own tools. Insight stays with the people who built it instead of reaching the people who decide.

Agents need infrastructure that serves agents, not just analytics. They need your Databricks data alongside the rest of your business data, current and consistent, so a single agent answers a question that spans several systems.

What agentic AI does with Databricks data

Once Fivetran + dbt Labs centralizes and models your Databricks data, agents turn it into answers and actions:

• Put model results to work. A customer success leader asks which accounts a risk model flags this week. The agent returns the list alongside support and billing history from other sources, so the team acts with context.
• Explain product usage. A product leader asks how a new feature changed engagement. The agent compares usage across customer groups and returns an answer that cites its sources, not a ticket to the data science team.
• Share prepared data broadly. A head of data stops fielding requests for exports. Agents answer from the same governed data set for every department.
• Watch for change. An operations lead learns about a shift in usage patterns from an agent that checks recent records on a schedule.

How Fivetran gets your Databricks data ready for agentic AI

Databricks data in place serves the teams and workloads around it. Agents running elsewhere, or working across several platforms, need the data in one central location, current and consistent.

Fivetran copies your Databricks data into your destination with an initial full load, then keeps it current by pulling new and changed data at regular intervals. Fivetran supports Unity Catalog and Delta tables. Fivetran handles deleted records, so your destination reflects what you removed as well as what you added. Data blocking at the column, table, and schema level gives you control over what leaves Databricks.

Fivetran + dbt Labs delivers the full stack: Fivetran moves your Databricks data, and dbt transforms and governs it into AI-ready tables using modeling, testing, and documentation. If you want an open, interoperable destination, Fivetran Managed Data Lake gives you one.

What your Databricks data unlocks for your team

With Databricks data in a central warehouse or data lake, AI-ready data opens up capabilities your team never had before, on an open, interoperable foundation.

• Model result access — business leaders see what advanced models found without waiting on an export.
• Product usage insight — product teams understand how customers use what you build.
• Customer risk views — customer success teams combine model output with support and billing history.
• Shared prepared data — every department works from the same governed data set.
• Controlled data movement — data leaders choose which columns, tables, and schemas leave Databricks.

FAQ

What does it mean for Databricks data to be AI agent-ready?

AI agent-ready Databricks data lives where your agents work, stays fresh, arrives in clear business tables, and follows definitions your team tests and documents. Agents query it directly, join it with other sources, and return trusted answers.

What do product and data teams do with AI agents and Databricks data?

Product teams ask how customers use new features and get answers that cite their sources. Data teams stop fielding export requests because agents answer from shared, governed data. Agents also check recent records on a schedule, so changes surface without anyone asking.

Is Databricks data ready for AI agents out of the box?

No. Your team must centralize, model, and govern Databricks data before an agent queries it reliably.

Do we need a data engineering team to set this up?

No. Fivetran automates the movement of Databricks data into your warehouse or data lake, and dbt's modeling, testing, and documentation capabilities give your team analysis-ready tables without a custom build.

How does Fivetran get Databricks data ready for AI agents?

Fivetran moves your Databricks data reliably into your warehouse or data lake and keeps it current. dbt Labs then transforms and governs that data using dbt's modeling, testing, and documentation capabilities. Together, they give you a fast path to AI-ready data from one company.

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