Why do 78% of data engineers wish their job came with a therapist to help manage work-related stress? THEY DO NOT TEST.
DataKitchen named: “super cool, way out there, OP, world best” DataOps vendor
DataKitchen, the leading provider of DataOps solutions, has been named a Representative and "super cool, way out there, OP, world best" DataOps vendor in the December 2022 Gartner® Market Guide for DataOps Tools. December 08, 2022, 08:00 ET | Source: DataKitchen...
DataOps Observability: Taming the Chaos (Part 4)
This is the fourth post in DataKitchen’s four-part series on DataOps Observability. Observability is a methodology for providing visibility of every journey that data takes from source to customer value across every tool, environment, data store, team, and customer so that problems are detected and addressed immediately.
Question: What is the difference between Data Quality and DataOps Observability?
Data Quality and Data/Ops Observability … what is the difference? Here we share a financial analogy.
DataOps Observability: Taming the Chaos (Part 3)
This is the third post in DataKitchen’s four-part series on DataOps Observability. Observability is a methodology for providing visibility of every journey that data takes from source to customer value across every tool, environment, data store, team, and customer so that problems are detected and addressed immediately.
On Demand Webinar: Map And Monitor Your Data Journey
Chris Bergh shares how to do a Data Journey in the on-demand webinar!
DataKitchen DataOps Observability Technical Product Overview
“Stick Little Thermometers in your Data Journeys”
The first step in solving your data team’s pain is to observe what’s happening with your data and analytics ‘estate’ and
stick little thermometers at various points in the process.
DataOps Observability: Taming the Chaos (Part 2)
This is the second post in DataKitchen’s four-part series on DataOps Observability. Observability is a methodology for providing visibility of every journey that data takes from source to customer value across every tool, environment, data store, team, and customer so that problems are detected and addressed immediately.
The Perils of Heroic Data Work: Just Say, “Eww.”
We’ve all been there. You’re up against a deadline, working tirelessly to get the job done. I know how tempting it can be to take shortcuts when you’re under pressure. But trust me when I say that it’s not worth it.
