Entrada

AI platforms are converging and fragmenting at the same time. A practitioner’s map of the modern AI stack – and where Databricks genuinely fits.

Poor ML architecture quietly drains budgets through retraining churn, drift, and shadow infrastructure. Here’s how to spot and fix it on Databricks.

Vibe-coding promises a working app in hours, but most of those apps hit the same wall: they’re disconnected from real business data, ungoverned, and expensive to run. Databricks’ new Genie App Builder, App Spaces, and Serverless Micro Apps are built to fix that, without slowing teams back down.

Most AI coding assistants treat code as the end product. In data platforms, code is just the interface, and Genie Code’s real advantage is reasoning across the governed tables, lineage, and pipelines behind it.

Every conversation with a CIO this year ends the same way: “Do we still need Power BI if we have Databricks Genie?” The honest answer is more interesting than yes or no. Here is what I tell clients before they rip out a working BI stack.

Most Databricks ML pipelines do not fail because the math is wrong. They fail because performance decisions made early quietly compound until cost, latency, and trust all start slipping at once.

Data + AI Summit 2026 brought the Entrada team back to San Francisco alongside more than 31,000 members of the data and AI community. As a pure-play Databricks partner, being there felt like standing at the epicenter of the next chapter of enterprise AI. A Few Proud Moments for the Entrada Team The Data + […]

For most of the last decade, the goal of a data platform was simple: make the data available. Land it, govern it, and let the humans take it from there. That goal is no longer enough. In 2026, the consumer of your enterprise data is increasingly likely to be something other than a human. It […]

Mortgage lenders sit on rich data across CRM, LOS, and servicing systems, yet still struggle to identify which borrowers are about to transact. Entrada’s Mortgage Intelligence Platform addresses that gap with a Databricks-native architecture: Cotality property intelligence delivered through Delta Sharing and Unity Catalog, deterministic scoring as governed SQL primitives, Genie grounded in a curated semantic layer, and Lakebase Postgres recording every approval and audit event. The result is a governed lead generation layer that tells growth teams who to contact, why now, and with what offer – and proves it afterward.

After years of architecting ML platforms on Databricks, one pattern keeps repeating: the difference between a model that survives in production and one that quietly fails usually comes down to how features are managed. Here’s what we’ve learned the hard way.