Data Engineering 
Data Strategy + Governance 
Data Migrations 
Concierge + Support 
Artificial Intelligence 
Training + Enablement 
Data Analytics + Insights 
Financial Services 
Communications, Media + Entertainment 
Healthcare + Life Sciences 
Technology + Digital Natives 
Manufacturing + Automotive 
Public Sector 
Retail + Consumer Goods 
Race to the Lakehouse 
AI + Data Maturity Assessment 
Unity Catalog 
Rapid GenAI 
Modern Data Connectivity 
Gatehouse Security 
Health Check 
Sample Use Case Library 
Blog 
Events 
About Us 
Careers 
Founding Team 
Genie App Builder: Fast, Governed AI Apps 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.
Beyond Autocomplete: How Databricks Genie Code Turns Data Work into Governed Agentic Workflows
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.
Databricks Genie vs Power BI and Tableau: Should You Add It, Replace, or Ignore?
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.
Why Your Databricks ML Pipelines Are Burning Cash (And How to Fix Them)
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.
Feature Store-Driven ML: Lessons from Real Deployments
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.
Governance Atlas: Databricks-Native Data Governance with Unity Catalog, Genie, and Lakebase
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.
Building an AI Billing Agent on Databricks: Anomaly Detection, Genie Analytics, and Governed Write-Back at Scale
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.
DataPact 3.0: Validation, Genie, and the discipline of a curated room
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.
From Cost Visibility to Action: Scaling FinOps Intelligence with Databricks System Tables and Genie
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.
Monitoring ML Models in Production with Databricks
Most ML models do not break in development. They break quietly in production, when data changes, performance drifts, and no one notices until business trust is already slipping. That is why monitoring is not an afterthought. It is one of the foundations of enterprise AI.