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.
Download the 2021 DataOps Vendor Landscape here. Read the complete blog below for a more detailed description of the vendors and their capabilities. DataOps is a hot topic in 2021. This is not surprising given that DataOps enables enterprise data teams to generate...
Orchestration of your environment pipelines enables you to seamlessly spin-up and manage repeatable workspaces that underpin your Production and Development pipelines. Include everything your team needs, such as servers, software, and test data, to work quickly and securely.
We share why and how to orchestrate your Development pipelines in order to quickly and confidently enhance and extend new analytics into your production systems.
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...
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,...
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...
Monitor every data journey in an enterprise, from source to customer value, in development and production.
Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change.
Data Production Teams
Data Science/AI
Data Engineering
Business Analytics
Data Observability
Data Mesh
DataGovOps
ModelOps / MLOps
Self-Service Operations
Data Products
Hybrid Cloud Architectures
Cloud Architectures
Kubernetes Architectures
Commercial Pharma Data Engineering
DataOps Transformation Advisory
Customer Data Platform Engineering
