Blog
The Equation For AI Success: DT + DX + CTX = 10x
How to Make Data Analysis Ten Times Faster with AI and Large Language Models
The DataOps Way to Data Quality: A Free Book for Every Data Team
Most data quality advice tells you what to measure. This book tells you why your team keeps failing and what to actually do about it.
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...
Data Quality Testing Is At The Core of Four Critical Data Team Processes
Every data and analytics team juggles multiple responsibilities. You are expected to ensure your data is accurate and fit for purpose. You need to prevent problematic data from entering your data environment. You must stop your data pipelines from delivering...
Stop Paying The Data Quality Tax
Why Data Quality And Data Observability Tools Cost So Much (And Why That's Ridiculous). You're paying enterprise prices for commodity algorithms. Here's what's really going on. We understand the struggle. Data pipelines fail, dashboards go stale, and someone in...
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...
