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Embracing Agility and Excellence in Data Operations: The DataKitchen DataOps Way
DataKitchen’s DataOps services are designed to empower teams at various stages of their DataOps adoption, providing a flexible and comprehensive roadmap to operational excellence
Key Success Metrics, Benefits, and Results for Data Observability Using DataKitchen Software
At DataKitchen, we would like to share some key success metrics of Data Observability Using DataKitchen DataOps Observability and DataOps TestGen.
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...
An AI Chat Bot Wrote This Blog Post …
Query> DataOps ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. DataOps involves collaboration between data engineers, data...
Gartner Market Guide to DataOps Software
We are excited that Gartner released its 'Market Guide to DataOps Tools'! The document they wrote is exceptionally close to what we see in the market and what our products do! This document is essential because buyers look to Gartner for advice on what to do and how...
DataOps Observability and Automation to the Rescue!
Data Team members, have you ever felt overwhelmed? The never-ending flow of new information can be stressful, and it's hard to know where to start. Well, don't worry because DataOps is here to help! In this post, we'll discuss how DataOps Observability and Automation...
DataOps Observability: Taming the Chaos (Part 4)
Part 4: Reviewing the Benefits This is the final 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,...
Question: What is the difference between Data Quality and DataOps Observability?
 Question: What is the difference between Data Quality and Observability in DataOps? Data Quality is static. It is the measure of data sets at any point in time. Data Observability is dynamic -- it is the testing of data, integrated data, and tools acting upon data...
DataOps Observability: Taming the Chaos (Part 3)
Part 3: Considering the Elements of Data Journeys 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...
“Stick Little Thermometers in your Data Journeys”
 Question:  What is something the data industry is missing? I think it's observability-led DataOps. I've come to believe that we, as an industry, will not change how people build things they've already made. They're already being Heroes and have pain, unhappiness,...
DataOps Observability: Taming the Chaos (Part 2)
Part 2: Introducing Data Journeys 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,...
The Perils of Heroic Data Work: Just Say, “Eww.”
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. But what happens when that "job" leaves a hairball of technical debt that will need to be fixed and improved tomorrow? And what...
DataOps Observability: Taming the Chaos (Part 1)
Part 1: Defining the Problems 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...
DataOps Mission Control And Managing Your Data Infrastructure Risk
Data Teams can't answer very basic questions about the many, many pipelines they have in production and in development. For example: Data Is there a troublesome pipeline (lots of errors, intermittent errors)? Did my source files/data arrive on time? Is the data in...
