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
The Art of Data Buck-Passing 101: Mastering the Blame Game in Data and Analytic Teams
In data and analytics, one skill stands timeless and universal: the art of blaming someone else when things go sideways. In this humorous blog, learn from the best!
ON DEMAND WEBINAR: Beyond Data Observability
Do you have data quality issues, a complex technical environment, and a lack of visibility into production systems?
These challenges lead to poor quality analytics and frustrated end users. Getting your data reliable is a start, but many other problems arise even if your data could be better. And your customers don’t care where the problem is in your toolchain. They want to know when to get their trusted dashboard refreshed (for example).
The uncertainty of not knowing where data issues will crop up next and the tiresome game of ‘who’s to blame’ when pinpointing the failure. It’s more than just a ‘last mile’ problem in data observability. It’s about personalization for your customers. Demanding Data Consumers require a personalized level of Observability.
Navigating the Chaos of Unruly Data: Solutions for Data Teams
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.
