Entrada

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.

Every serious governance project eventually reaches the same uncomfortable moment: the platform has the metadata, but the organization still does not have a product. There is a catalog. There are tags. There are comments, owners, lineage events, audit rows, dashboards, policies, and a dozen local rituals around who is allowed to change what. Yet when a steward asks, “Can I safely change this field?”, the answer still arrives as a meeting, a spreadsheet, and a prayer.

Inside the Customer Billing Accelerator from Entrada and Databricks, an agentic AI stack that detects anomalies, answers finance questions in plain English, and writes back to source systems, all governed through Unity Catalog.

A field report on what changed between DataPact 2.9 and 3.0, why we put a managed Genie space at the centre of the release, and the engineering it takes to make a conversational data quality surface trustworthy enough to call a product.

This post walks through the architecture Entrada built around that observation, the Serverless Cost Control Accelerator, and, more importantly, the design principles behind it. Regardless os whether we’re a platform engineer, SRE, or FinOps lead trying to decide where to invest, the principles matter more than the product.