DataOps Observability and Test Automation Software that rapidly finds and fixes problems across your complex data estate. Get observing in 60 minutes.
Data Journey Observability Software: When ‘Failure Is Not An Option.’
DataKitchen provides software to observe and validate every data journey in an organization, from source to customer value, in development and production, so that data teams can deliver insight to their customers with virtually no errors and a rapid rate of new insight creation.
Our software allows data and analytic teams to observe complex end-to-end processes, generate and execute tests, and validate the data, tools, processes, and environments across their entire data analytics organization, providing massive increases in quality, cycle time, and team productivity.
Data Journey Reliability. Delivered.
Data breaks. Servers break. Your toolchain breaks. We ensure your team is the first to know and the first to solve with visibility across and down your Data Journey.
Reduce Errors to ZERO
Win the trust and confidence of your business customers by eliminating errors in your analytics. What’s more, less time spent on unplanned work means more time spent on innovation.
Find Problems Before Your Customers
Stop the embarrassment of incorrect data, dashboards, models, or even just being late. Stop wasting time on data fire drills. Protect yourself from data provider ineptitude.
Preventing Problems From Happening Again
Spend less time worrying about what may go wrong by observing the entire data journey and get more time to create by permanently preventing problems from happening again.
Want to See Our DataOps Observability, TestGen, and Automation Software Products in Action?
Why Use DataKitchen’s Software?
“We realized dramatics cost savings and also capitalized on opportunities better because we were more efficient and could do things more quickly.”
What’s Cooking at DataKitchen
Data teams have out-of-control databases/data lakes, with many users and tools constantly changing data, many users and tools out of their control, and an unknown/uncontrolled ETL/ELT process with no data quality tests. As a result, they are left with the blame for bad data and have limited ways to affect the actions of others who are changing the data. They need help to quickly identify anomalies and problems in the data before someone finds it.
The DataOps Cookbook