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  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,...

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." 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...

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...

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...

DataOps is a discipline focused on the delivery of data faster, better, and cheaper to derive business value quickly. It closely follows the best practices of DevOps although the implementation of DataOps to data is nothing like DevOps to code. This paper will focus...