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.
DataOps Mission Control And Managing Your Data Infrastructure Risk
The Head of data got a call from the CEO of the entire company about a compliance report that was empty, with no data. So, he had to rally 26 different people across his team all-day And what was the problem? A field passed through the pipeline that was blank. Can you imagine how embarrassed he is at the error? How frustrated all those 26 people — most likely the best he has on his team — at having to chase a crappy error? And he has 1000 other pipelines in the same ‘hope it works’ position, just waiting for some customer to find a problem. High risk, indeed.
DataKitchen Named a Representative Vendor in the 2022 Gartner® Data and Analytics Essentials: #DataOps Report
“The goal of DataOps is to enable predictable delivery and change management of data and all data-related artifacts such as data pipelines, data models and semantics”
IBM Loves DataOps
IBM Recently published a great white paper on DataOps. We want to share it with you!
