Blog
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.
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 are now supported by open source and enterprise TestGen.
Data Quality vs. Data Observability: The Pets and Cattle of Your Data Estate
Data Quality vs. Data Observability: The Pets and Cattle of Your Data Estate If you've spent any time in DevOps circles, you've heard the phrase "cattle, not pets." Randy Bias popularized this analogy around 2012 to explain a fundamental shift in how we think about...
How TestGen Complements Microsoft Purview for Enterprise Data Quality
How does DataKitchen's TestGen complement Microsoft's Purview? Organizations that deploy Microsoft Purview gain a powerful foundation for data governance, cataloging, and lineage across their Microsoft ecosystem. But once teams begin governing data assets, they...
The Five Organizational Sins of Data Teams
In the first part of this series, we explored the Seven Deadly Sins of Data Quality, focusing on the individual behaviors that sabotage data teams: shame, denial, avoidance, passivity, laziness, gluttony, and ignorance. But here is the uncomfortable truth that nobody...
The Data Consultant’s Growth Playbook: Accelerating Client Acquisition and Retention with DataKitchen’s Open Source TestGen
https://datakitchen.io/partner-request/If you’ve worked in data and analytics consulting, you know the hardest part isn’t always the technical side. The real challenge is finding new clients, growing existing accounts, and proving your value to justify your fees. The...
The Seven Deadly Sins of Data Quality
Data quality problems don't just appear out of nowhere. They fester in organizational cultures where dysfunction becomes normalized, where warning signs are ignored, and where the path of least resistance leads to ever-growing data debt. After years of working with...
The Data Errors Shame Game: Why Data Engineers Avoid Harsh Truths
Here's something we don't talk about enough in data engineering: shame. The quiet, corrosive feeling that if something is wrong with the data, it must be your fault. And if you're the one who finds the problem, you're the one who'll have to answer for it. This shame...
The Ostrich Problem: Your Data Team Thinks Their Job Ends at Deployment.
Your team has gone full ostrich. Heads buried deep in the sand, convinced that if they can't see the production data errors, those errors must not exist. The pipeline is deployed. The tests passed. Job done, right? Meanwhile, you're the one fielding angry Slack...
Peter Piper on the Four Ps of AI Data Quality: Purge, Patch, Push Back, or Pass
How does a data team prevent poor data from poisoning AI when they have piles of raw and imperfect data? Teams responsible for data used to train AI models (e.g., LLMs) face a persistent problem: piles of raw, imperfect data. Pressure builds to process quickly,...
The 2026 Open Source Data Profiling Software Landscape
🔥 TLDR: What You’ll Learn in This Article AI has fundamentally changed the stakes: bad data no longer just breaks dashboards—it can actively mislead your LLMs and automated decision systems. Data profiling has re-emerged as the most essential first step in protecting...
The 2026 Data Quality and Data Observability Commercial Software Landscape
With 50+ vendors to choose from, data quality software has never been more powerful, more plentiful, or more confusing—until now. Let's be honest: the data quality and observability market has become a jungle—and it's consolidating fast. Datadog swallowed Metaplane to...
Sure, Go Ahead And Feed That Data To The LLM … What Could Possibly Go Wrong?
Data teams today face a harsh reality: frequent pipeline failures, reactive fixes, and poor data quality often lead to bad decisions. Just when we thought we understood our data challenges, Large Language Models (LLMs) radically expand the use and scope of data ......
Webinar: Data Quality, DataOps, and Large Language Models
Data Quality, DataOps, and Large Language Models Struggling to bring order to the data and AI chaos?The reality for many data teams is often an unproductive mix of broken pipelines, reactive problem-solving, and “good enough” data that leads to poor...
