We asked an AI Chat Bot (https://chat.openai.com/chat) a few questions about DataOps. It got a bit silly at the end.
Gartner Market Guide to DataOps Software
Gartner released its ‘Market Guide to DataOps Tools’ The document they wrote is exceptionally close to what DataKitchen sees in the market and what our products do! Here is our take on the guide.
DataOps Observability and Automation to the Rescue!
Feeling overwhelmed by your data job? Don’t worry, DataOps is here to help! This post discusses how DataOps Observability and Automation can relieve team stress and show you how to get started.
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.
“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.
DataOps Observability: Taming the Chaos (Part 1)
This is the first 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. DataKitchen has released the first version of its Observability product, which implements the concepts described in this series.
