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.
Data Quality and Data/Ops Observability … what is the difference? Here we share a financial analogy.
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.
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.
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.
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.
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.
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.
“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”
Analytics are prone to frequent data errors and deployment of analytics is slow and laborious. The strategic value of analytics is widely recognized, but the turnaround time of analytics teams typically can’t support the decision-making needs of executives coping with...
DataOps Data Quality TestGen:
Simple, Fast, Generative Data Quality Testing, Execution, and Scoring.
[Open Source, Enterprise]
DataOps Observability:
Monitor every date pipeline, from source to customer value, & find problems fast
[Open Source, Enterprise]
DataOps Automation:
Orchestrate and automate your data toolchain with few errors and a high rate of change.
[Enterprise]
DataOps Consulting, Coaching, and Transformation
Commercial Data & Analytics Platform for Pharma
Data Production Teams
Data Science/AI
Data Engineering
Data Quality
Business Analytics
Data Products
Data Mesh
Data Contracts
ModelOps / MLOps
DataGovOps
Self-Service Operations
Data Quality Assessments
Data Quality Testing
Data Observability
Data Orchestration
Monitor every Data Journey in an enterprise, from source to customer value, in development and production.
Simple, Fast Data Quality Test Generation and Execution. Your Data Journey starts with verifying that you can trust your data.
Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change.
