Hi Data and AI Pros, Your Databricks lakehouse is built for AI-scale analytics. The pipelines feeding it often are not.

When those pipelines are brittle, batch-heavy, or missing sources, the lakehouse can only work with what reaches it. Every model, dashboard, and agent sitting on top inherits the same gaps.

On June 25 at 10am ET, CData is running a live session on the part of the stack most teams underinvest in: getting data in. The focus is CData Sync, and how it keeps a Databricks environment analytics-ready and AI-ready without custom code or the overhead of stitching connectors together.

Here is what the session covers:

• How to connect hundreds of data sources to Databricks using pre-built connectors. No custom code, no brittle workarounds.

• How CDC pipelines push near real-time, incremental updates straight into Delta Lake and Iceberg tables, so freshness keeps up with AI and agentic workloads.

• How built-in orchestration inside CData Sync removes the need for a separate pipeline tool, which cuts complexity and operational load.

If you own the data flowing into Databricks, the bottleneck is rarely the lakehouse itself. It is everything upstream of it. This session is a practical look at closing that gap.

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Everyone is racing to better models. The bottleneck was never the model. It is the data layer underneath it. That is where the value lives. Where the risk lives. Where the speed lives. And in the agentic era, something changed. Agents do not run on a fixed script of pre-set queries. They reason, decide, and act on data directly, at machine speed.

That turns the database from passive plumbing into the place the work actually happens. So it has to be fast, accurate, and governed in real time.

Here is the part the market keeps proving. Databricks bought Neon. Snowflake bought Crunchy Data. Both spending heavily to bolt operational Postgres onto an analytics platform.

EDB is coming from the opposite direction. It has been the open source operational Postgres core for twenty years, and is now extending out into analytics, vector, and agentic workloads from that same foundation. The whole market is converging on the operational database where EDB already sits.

On June 25, EDB brings relational, analytical, vector, and agentic workloads into one Postgres engine, governed at the data layer, on infrastructure you own.

A few things I am paying attention to.

An agentic database that monitors 200+ metrics and, where policy allows, applies the fix itself. Tuning up to 10x faster.

Converged analytics with WarehousePG. Real-time to petabyte scale, zero ETL, up to 58% lower TCO.

Governance held at the data layer. Native Postgres roles and row-level security, not a separate control plane bolted on top. Every agent action held to a human’s rigor, with full session-level audit.

I will be tuning in for the live session and demos with Kevin Dallas (CEO), Nancy Hensley (Chief Product Officer), Avijit Sinha (SVP Corporate Development), and Quais Taraki (CTO).

40 minutes. Live or on-demand.

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Best,
Ravit Jain
Founder & Host of The Ravit Show