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Databricks-Native MDM: Introducing Golden Lake for Entity Resolution
Golden Lake is Entrada’s Databricks-native MDM and entity resolution accelerator, built to resolve your most valuable entities without ever moving data out of Unity Catalog. Master Data Management (MDM) is a constant source of tension for data-driven organizations. There is a crucial need to obtain a unified, 360-degree perspective of core entities within their data, […]
Databricks Semantic Layer: 10 Proven AI Agent Practices
Best practices for Databricks semantic layers, ontologies, and Unity Catalog glossaries.
The Future of AI Platforms: Where Databricks Fits
AI platforms are converging and fragmenting at the same time. A practitioner’s map of the modern AI stack – and where Databricks genuinely fits.
The Architecture Tax: Why Your ML Models Cost More Than They Should
Poor ML architecture quietly drains budgets through retraining churn, drift, and shadow infrastructure. Here’s how to spot and fix it on Databricks.
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.