DataOps | Develop & Deploy Data Analytic Pipelines

Our end-to-end DataOps platform automates and coordinates all the people, tools and activities in your pipeline – everything from orchestration, testing, and monitoring to development and deployment.

It’s DataOps, Delivered.

Orchestrate With the Tools You Love

You’ve already got the tools you need. Our platform automatically orchestrates your end-to-end multi-tool, multi-environment pipelines – from data access to value delivery.

Reduce Errors to Virtually Zero

Catch embarrassing errors before they reach the end-user by adding any number of automated tests at every node in your development and production pipelines.

Foster Innovation with Safe Workspaces

Spin-up self-service work environments in minutes to enable teams to make changes and experiment – without breaking production.

Deploy Faster than the Speed of Business

Fearlessly deploy new features into production with the push of a button. Free your teams from tedious, manual work that impedes innovation.

What’s Cookin’ at DataKitchen

Catch the latest news and happenings from DataKitchen.

10 Hot Big Data Companies To Watch In 2020

Gartner Research Names DataKitchen “Cool Vendor”

Methodologies and Tools That Reduce Analytics Cycle Time While Improving Quality

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Customer Reviews

Case Study: Celgene's Product Launch

Celgene. See how a leading pharmaceutical company used DataOps to significantly improve product launch success and:

Reduce cycle time from weeks (or months) to just one day.
Reduce errors to practically zero (just one per year!).
Dramatically increase visualization output each week (from 50 to 1500).
Dramatically increase staff productivity

Read the Case Study and Blog Post

Case Study: Catholic Relief Services

CRS One of the largest international relief organizations used DataOps to understand their donors and increase funds to help 130 million people in more than 90 countries. With DataOps, the team was able to update their database daily and make real-time changes in Tableau, while still using the tools they love.

Why is Great Data Analytics So Difficult? Iteration

Innovation in Analytics Requires Iteration. And Iteration is Hard

Speed: Innovative insights cannot be delivered at the speed of business
Quality: Data Quality can be compromised if testing is not pervasive and automated
Reproducibility: Experimentation requires a safe places to work that reflects what is in production
Diversity: Plethora of teams roles, tools, and data conspire to create complexity

Read About the Seven Steps To DataOps

Successful projects generate lots of requests

Your Team Has Their Tools. Many Of Them.

You need to work with the tools you already have: Databases, Data Engineering/ETL tools, BI Tools, Data Science Tools, and Data Governance Tools. Whew that's a lot.

You Don't Think You Run a Factory? You Do.

DataOps Runs the Assembly Line from Data to Customer Value. Data Analytic processes are like manufacturing: materials (data), steps (your analytic tools and code) and production outputs (refined data, charts, graphs, model)

You Want To Succeed? Try our DataOps Platform

Automatically deliver and operate all your data analytic pipelines — simply, collaboratively, and at enterprise scale — with DataKitchen’s DataOps Platform.

We deliver previously unavailable business insights by enabling the deployment, monitoring, and coordination of multi-tool, multi-team, and multi-location data analytic pipelines. Iterate to Innovate.

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