Blog
Impact Dimensions Cure A Data Quality Blind Spot
Your data quality tools are like that neighbor who won’t stop describing his rash at parties — great at describing problems, terrible at telling you what to do about them. Impact Dimensions cut through the noise by organizing data quality issues into four categories that reveal which problems actually matter, where to fix them cheapest, and why the ones you’re ignoring may be costing you the most.
Context As Infrastructure: The Six-Category Framework for AI-Ready Data Analysis
The six categories of context in this post are not just documentation—they are infrastructure. This layer sits between your data and your AI, transforming schemas into meaning, tables into business concepts, and raw query results into answers your analysts can trust.
Data Production Tripwires in Databricks: Stop Bad Data Before It Reaches Production
Your job finished green. The number was wrong. That is the failure mode that keeps data engineers up at night — not the pipeline crash you can see in the logs, but the quiet corruption that passes through every check and lands in a dashboard where a VP is about to...
Introducing BuzzOps: A Tool to Translate Vendor BS. You’re Welcome.
Introducing BuzzOps: A Tool to Translate Vendor BS. You're Welcome. These days, every vendor claims to be agentic, AI-native, and context-aware. We made a tool that explains what they really do. Data engineering and analytics are now overflowing with buzzwords. As...
TestGen Now Supports Oracle and SAP HANA, with a New Setup Wizard to Get You Running Fast
Two of the most common databases in large enterprises have historically been outside TestGen's reach. Oracle sits at the center of ERP systems, clinical data warehouses, and operational systems of record across pharma, financial services, and manufacturing. SAP...
DataKitchen Enhances Its Support for Data Stewards to Manage Data Quality Tests in TestGen
Data stewards bear a difficult responsibility. They are the people in the organization who are supposed to define, document, and enforce data quality standards, yet in most organizations, they lack the tools to do so. They can't easily scope who sees what. They can't...
$1 Billion in Data Observability VC Investment: This Is Not Going to End Well
Too Much Money, Too Many Vendors, Your Problem You have probably noticed that your inbox is full of data observability or data quality vendor pitches, your conference has a dozen booths for tools that all look roughly the same, and somehow every budget conversation...
“We Just Eyeball Row Counts and Pray”
What Internet Communities Really Think About Why Data Engineers Don't Test TL;DR: We read 849 comments across 18 community threads on Reddit, Hacker News, Stack Overflow, and the dbt Community Forum. The #1 reason data engineers don't test: nobody gives them the time....
DataOps + FITT + Data Testing = 10x Data Engineering Productivity with AI
10x Your Data Engineering with AI: Practitioner Patterns That Actually Work Claude Code ( DataOps + FITT + Data Testing ) = 10x Data Engineering Productivity AI coding tools like Claude Code are generating significant excitement in software engineering. But for data...
The Equation For AI Success: DT + DX + CTX = 10x
Everyone wants AI to answer business questions with their data. The reality is most AI assistants built on enterprise data give mediocre, unreliable, or outright wrong answers — not because the AI is bad, but because the foundation underneath it is. Your AI chatbot is...
The DataOps Way to Data Quality: A Free Book for Every Data Team
We announce our third book, The DataOps Way To Data Quality & Data Observability. Data quality is not a technology problem. It never has been. The data industry has spent years chasing the next tool, platform, or vendor promise, yet data errors still dominate the...
Why Your Data Quality Dashboard Isn’t Working And What to Do About It
Why Your Data Quality Dashboard Isn't Working And What to Do About It Your organization has a data quality dashboard. You’ve put time and effort into visualizing metrics, tracking scores, and creating reports. But week after week, data quality barely improves. This...
We Got Roasted On Reddit For Asking ‘Why Data Engineers Don’t Test?’
How This Research Started (And Why We're Telling You) DataKitchen co-founder Gil Benghiat recently posted a question to r/dataengineering — one of the largest and most active data engineering communities on the internet. The question was genuine: "What is actually...
Be The First To Know: Smart, Continuous Table Monitoring Has Arrived In TestGen
If you're a data engineer tired of being the last to hear when something goes wrong, this is for you. You know the feeling: a business stakeholder messages you at 2 PM on a Tuesday saying, "Hey, the revenue numbers look off." You open your laptop and spend the next...
