Christopher Bergh detailed the company’s release of new open-source tools to enhance DataOps practices by addressing common inefficiencies and errors within data teams. During the webinar, he demonstrated how these tools provide robust data observability and automated testing to improve productivity and reliability across data operations.
Why We Open-Sourced Our Data Observability Products
Why open source DataOps Observability and DataOps TestGen? Our decision to share full-featured versions of these products stems from DataKitchen’s long-standing commitment to enhancing productivity for data teams and promoting the use of automated, observed, and trusted tools. It aligns with our company’s philosophy of sharing knowledge and now software to inspire teams to implement DataOps effectively.
Webinar Summary: Agile, DataOps, and Data Team Excellence
Gil Benghiat, co-founder of Data Kitchen, began by explaining the overarching goal of achieving data team excellence, which involves delivering business value quickly and with high quality. He detailed data teams’ everyday challenges, such as balancing speed and quality, and the impact of Agile methodologies borrowed from software development practices.
Congratulations to the Karuna Team for their acquisition!
Today, Bristol Myers Squib (BMS) has fully acquired Karuna Therapeutics. We congratulate our customer on an amazing success.
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.
ngx-toolkit, a new open-source project from DataKitchen
At DataKitchen, we use Angular and strive for well-tested and maintainable code. We’ve created three libraries that have helped accelerate Angular development in our software projects. We are proud today to present these to the open source community.
Why Not Hearing About Data Errors Should Worry Your Data Team
Just because you’re not hearing about data errors doesn’t mean they don’t exist. This silence could be a ticking time bomb for underlying issues yet to surface. Here are seven compelling reasons why you should care and be proactive, even when all seems well.
Your LLM Needs a Data Journey: A Comprehensive Generative AI Guide for Data Engineers
Large Language Models (LLMs) and Generative AI are all the rage right now but will only work for organizations that have a solid grasp on the quality of their data and the series of operations acting upon that data to augment the base LLM.
DataKitchen Resource Guide To Data Observability & DataOps
A list o the best Data (and Analytic) Observability & Data Journey – Ideas and Background Links