Databricks Data + AI Summit 2025: Five takeaways for data professionals, developers

At Databricks’ Data + AI Summit 2025 last week, the company showcased a variety of generative and agentic AI improvements it is adding to its cloud-based data lakehouse platform — much as its rival Snowflake did the previous week.

With the two competitors’ events so close together, there’s little time for their product engineering teams to react to one another’s announcements. But that also means they’re under pressure to announce products that are far from ready for market, so as to avoid being scooped, perhaps explaining the plethora of features still in beta-testing or “preview.”

Here are some of the key new products and features announced at the conference that developers and data professionals may, one day, get to try for themselves:

It’s automation all the way down

Many enterprises are turning to AI agents to automate some of their processes. Databricks’ answer to that is Agent Bricks, a tool for automating the process of building agents.

It’s an integral part of the company’s Data Intelligence platform, and Databricks s pitching it as a way to take the complexity out of the process of building agents, as most enterprises don’t have either the time or the talent to go through an iterative process of building and matching an agent to a use case. It’s an area that, analysts say, has been ignored by rival vendors.

Another way that Databricks is setting its agent-building tools apart from those of rival vendors is that it is managing the agent lifecycle differently — not from within the builder interface but via Unity Catalog and MLflow 3.0.

Currently in beta testing, the interface supports the Model Context Protocol (MCP) and is expected to support Google’s A2A protocol in the future.

Eliminating data engineering bottlenecks

Another area in which Databricks is looking to help enterprises eliminate the data engineering bottlenecks that slow down AI projects is in data management. It previewed a data management tool powered by a generative AI assistant, Lakeflow Designer, to empower data analysts to take on tasks that are typically processed by data engineers.

Lakeflow Designer could be described as the “Canva of ETL,” offering instant, visual, AI-assisted development of data pipelines, but Databricks also sees it as a way to make collaboration between analysts and engineers easier.

Integrated into Lakeflow Declarative Pipelines, it also supports Git and DevOps flows, providing lineage, access control, and auditability.

Democratizing analytics for business users while maintaining governance

Databricks also previewed a no-code version of its Data Intelligence platform called Databricks One that provides AI and BI tools to non-technical users through a conversational user interface.

As part of the One platform, which is currently in private preview and can be accessed by Data Intelligence platform subscribers for free, Databricks is offering AI/BI Dashboards, Genie, and Databricks Apps along with built-in governance and security features via Unity Catalog and the Databricks IAM platform.

The AI/BI Dashboards will enable non-technical enterprise users to create and access data visualizations and perform advanced analytics without writing code. Genie, a conversational assistant, will allow users to ask questions about their data using natural language.

Databricks has also introduced a free edition of its Data Intelligence platform — a strategy to ensure that more data professionals and developers are using the platform.

Integrating Neon PostgreSQL into the Data Intelligence platform

One month after acquiring Neon for $1 billion, the cloud lakehouse provider has integrated Neon’s PostgreSQL architecture into its Data Intelligence Platform in the form of Lakebase.

The addition of the managed PostgreSQL database to the Data Intelligence platform will allow developers to quickly build and deploy AI agents without having to concurrently scale compute and storage while preventing performance bottlenecks, simplifying infrastructure issues, and reducing costs.

Integrated AI-assisted data migration with BladeBridge

Databricks has finally integrated the capabilities of BladeBridge into the Data Intelligence platform, after acquiring the company in February, in the form of Lakebridge — a free, AI-assisted tool to aid data migration to Databricks SQL.

Earlier this month, Databricks rival Snowflake also introduced a similar tool, named SnowCovert, that uses agents to help enterprises move their data, data warehouses, business intelligence (BI) reports, and code to Snowflake’s platform.

Other updates from Databricks included expanded capabilities of Unity Catalog in managing Apache Iceberg tables.