This article was originally published in Forbes. Engineers unleashed artificial intelligence (AI) bias, and it will be engineers who design the solutions that eliminate it. Authors of an article published by McKinsey Global Institute assert that “more human vigilance...
A survey commissioned by data.world and DataKitchen reveals a disturbing state of affairs among data engineering professionals. The study of 600 data engineers, conducted by Wakefield Research, suggests an overwhelming majority are burned out and calling for relief....
A DataOps Engineer owns the assembly line that's used to build a data and analytic product. Data operations (or data production) is a series of pipeline procedures that take raw data, progress through a series of processing and transformation steps, and output...
Wayne Eckerson discusses the critical capabilities required for DataOps success and how to compare different vendors’ DataOps offerings
Data organizations don’t always have the budget or schedule required for DataOps when conceived as a top-to-bottom, enterprise-wide transformational change. An essential part of the DataOps methodology is Agile Development, which breaks development into incremental...
Adopting DataOps can be easy; by following DataKitchen’s ‘Lean DataOps’ four-phase program, you can roll out DataOps in smaller, easy-to-manage increments.
Chris Bergh chats with author Randy Bean about his book, Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data & AI.
In this five-module course, Mike Lampa & Chris Bergh teach data professionals to plan their organization’s DataOps program for low errors & fast deployment.
“How do you make it so that your data and analytics team can get the ideas from their heads into production quicker? How do you create a process like a factory that produces Toyotas and not AMC Pacers? How can you get these teams who work all over the company to collaborate better? Those are the problems that DataOps solves.”
Cognizant’s JP Thakur & DataKitchen’s Chris Bergh discuss how DataOps sets the foundation for Data Modernization initiatives enabling continuous data & insight.
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.