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.
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.
Today, Bristol Myers Squib (BMS) has fully acquired Karuna Therapeutics. We congratulate our customer on an amazing success.
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
At DataKitchen, we would like to share some key success metrics of Data Observability Using DataKitchen DataOps Observability and DataOps TestGen.
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.
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.
A list o the best Data (and Analytic) Observability & Data Journey – Ideas and Background Links
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!
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.
DataOps Observability: Monitor every Data Journey in an enterprise, from source to customer value, and find errors fast! [Open Source, Enterprise]
DataOps Data Quality TestGen: Simple, Fast Data Quality Test Generation and Execution. Trust, but verify your data! [Open Source, Enterprise]
DataOps Automation: Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change. [Enterprise]
DataOps Consulting, Coaching, and Transformation
Commercial Data & Analytics Platform for Pharma
Data Production Teams
Data Science/AI
Data Engineering
Data Quality
Business Analytics
Data Products
Data Mesh
Data Contracts
ModelOps / MLOps
Data Observability
DataGovOps
Self-Service Operations
Hybrid Cloud Architectures
Cloud Architectures
Kubernetes Architectures
Monitor every Data Journey in an enterprise, from source to customer value, in development and production.
Simple, Fast Data Quality Test Generation and Execution. Your Data Journey starts with verifying that you can trust your data.
Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change.