DataOps places great emphasis on productivity. Team velocity can improve significantly when the data organization uses tools and methods that promote component reuse. Architecting analytics as microservices is one way to stimulate code reuse. The challenge is that the...
Breaking Through the Noise A Guide to Understanding DataOps Solutions DataOps is the hot topic on every data professional’s lips these days, and we expect to hear much more about DataOps in 2020. This is not surprising given that DataOps holds true potential for...
Leading software companies perform millions of code releases per year. Typical data-analytics organizations perform less than 10. This gap explains why most data-analytics projects fail to deliver. Without the capability to move at lightning speed, data analytics...
The “Right to Repair” Data Architecture with DataOps We’ve been attending data conferences for over 20 years. It has been common to see presenters display a data architecture diagram like the (simplified) one below (figure 1). A data architecture diagram shows how raw...
A Guide to the Six Types of Data Quality Dashboards
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 Pharma Agile Data Warehouse
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.