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...
Tests identify data and code errors in the analytics pipelines. Automated orchestration of tests is especially important in heterogeneous technical environments with streaming data. The DataKitchen Platform makes it easy to write tests that check and filter data...
The technical architecture that powers steaming analytics enables terabytes of data to flow through the enterprise’s data pipelines. Real-time analytics require real-time updates to data. To that end, data must be continuously integrated, cleaned, preprocessed,...
How much of your time do you spend writing tests? In a DataOps enterprise, data professionals spend 20% of their time writing tests. This may seem like a lot, but if an organization is wasting time due to errors, every hour spent on tests saves many hours in lost...
Announcing Open Source DataOps Data Quality TestGen 3.0
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]
DataOps Automation:
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
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.