Learn how to add data quality gates directly into your Databricks job DAG so that bad data stops at the layer where it fails — and never touches production.
$1 Billion in Data Observability VC Investment: This Is Not Going to End Well
VC overinvestment causes predatory usage-based pricing and threatens vendor sustainability. How many data engineers will you need to lay off to cover your data observability costs this year?
“We Just Eyeball Row Counts and Pray”
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. The #2 reason: the data changes faster than the tests can keep up. Here’s the full breakdown, in their own words.
FITT vs. Fragile: SQL & Orchestration Techniques For FITT Data Architectures
Transform data engineering from a high-stress, “hero saves the day” kind of job into something systematic and predictable that actually scales as your team and business grow. Stop babysitting pipelines: SQL & ELT the FITT Way.
We’ve Been Using FITT Data Architecture For Many Years, And Honestly, We Can Never Go Back
Most data architectures are designed to maximize data vendor revenues, not data team productivity. Let us show you how a Functional, Idempotent, Tested, Two-Stage (FITT) data architecture can deliver better productivity, reliability, and happiness.
The $100 Billion Secret: Why Leading Pharma Companies Outsource Their Commercial Data Teams
Because the real differentiator in today’s market isn’t just having data. It’s about having it simplified, integrated, trusted, and continually improving. It’s having a data team you can trust. And control.
Drug Launch Case Study: Amazing Efficiency Using DataOps
When launching a groundbreaking pharmaceutical product, the stakes and the rewards couldn’t be higher. This blog dives into the remarkable journey of a data team that achieved unparalleled efficiency using DataOps principles and software that transformed their analytics and data teams into a hyper-efficient powerhouse.
From Cattle to Clarity: Visualizing Thousands of Data Pipelines with Violin Charts
What do you do when you have thousands of data pipelines in production? Is there a way that you can visualize what is happening in production quickly and easily?
Data Quality Circles: The Key to Elevating Data and Analytics Team Performance
DataOps Quality Circles are focused teams within data and analytics organizations that meet weekly or monthly to drive continuous improvement, quality automation, and operational efficiency. By leveraging the principles of DataOps, these circles ensure that data processes are error free, consistent, and aligned with business goals.
2024 Gartner Market Guide To DataOps
We celebrate Datakitchen’s leadership in the 2024 Gartner Market Guide to DataOps. DataKitchen has the most complete, feature-rich, open, and modular DataOps product suite in the industry and the expertise to help you succeed
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Impact Dimensions Cure A Data Quality Blind SpotApril 16, 2026
