In a previous blog, we talked about aligning technical environments to facilitate the migration of analytics from development to production. In this post, we will introduce the concept of “Kitchens” and illustrate how they simplify the deployment of data analytics. In...
Data analytics is performed using a seemingly infinite array of tools. In larger enterprises, different groups can choose different cloud platforms. Multi-cloud or multi-data-center integration can be one of the greatest challenges to an analytics organization....
Production engineers and analytics developers sometimes row the enterprise boat in different directions. Perhaps it is their roles and objectives which keep them at odds. It’s the production engineer’s duties to keep data operations up and running. Innovation is good,...
Although work restrictions are being lifted around the country, the office work environment will probably never be the same. Many companies are allowing employees to work remotely through the end of the year, until they are comfortable returning, or even forever...
DataOps improves your ability to orchestrate your data pipelines, automate testing and monitoring, and speed new feature deployment. DataOps recognizes that, in any data project, many different tools play an important role as independent components of the data...
Quantum computing will fail before it succeeds. That’s not a criticism of quantum computing. It’s more a commentary on the difficulty of deploying solutions based on cutting-edge innovation. In 2020, the human species has extensive experience with new technologies. I...
In today’s on-demand economy, the ability to derive business value from data is the secret sauce that will separate the winners from the losers. Data-driven decision making is now more critical than ever. Analytics could mean the difference between finding the right...
The primary source of information about DataOps is from vendors (like DataKitchen) who sell enterprise software into the fast-growing DataOps market. There are over 70 vendors that would be happy to assist in your DataOps initiative. Here’s something you likely won’t...
A Guide to Ops Terms and Whether We Need Them It is challenging to coordinate a group of people working toward a shared goal. Work involving large teams and complex processes is even more complicated. Technology-driven companies face these challenges with the added...
DataOps is the art and science of automating the end-to-end life cycle of data-analytics to improve agility, productivity and reduce errors to virtually zero. The foundation of DataOps is orchestrating three pipelines: data operations, analytics development/deployment...
DataOps Data Quality TestGen:
Simple, Fast, Generative Data Quality Testing, Execution, and Scoring.
[Open Source, Enterprise]
DataOps Observability:
Monitor every date pipeline, from source to customer value, & find problems fast
[Open Source, Enterprise]
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Orchestrate and automate your data toolchain 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
DataGovOps
Self-Service Operations
Data Quality Assessments
Data Quality Testing
Data Observability
Data Orchestration
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.
