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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

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...

IBM Loves DataOps

IBM Loves DataOps

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...

Fire Your Super-Smart Data Consultants with DataOps

Fire Your Super-Smart Data Consultants with DataOps

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 with Matillion and DataKitchen

DataOps with Matillion and DataKitchen

The Matillion data integration and transformation platform enables enterprises to perform advanced analytics and business intelligence using cross-cloud platform-as-a-service offerings such as Snowflake. The DataKitchen DataOps Platform provides a way to extend...

DataOps For Business Analytics Teams

DataOps For Business Analytics Teams

Business analysts often find themselves in a no-win situation with constraints imposed from all sides. Their business unit colleagues ask an endless stream of urgent questions that require analytic insights. Business analysts must rapidly deliver value and...

DataKitchen’s Best of 2021 DataOps Resources

DataKitchen’s Best of 2021 DataOps Resources

Before we shut the door on 2021, we would like to share our most popular DataOps content in hopes that it can help you as you learn about and implement DataOps. We hope you and your family have happy holidays and we look forward to continuing your DataOps journey with...

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