DataKitchen: The DataOps Company

Data Operations Drive Success


It’s not about new tools, a bigger team, or even more data.

Success in Data Analytics comes from quickly and repeatedly delivering value to your customers.


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.

Your Data Analytic ToolChain

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)

Orchestrate data to value.

You Want Continuously Change Production? Be Agile.

DataOps Speeds Ideas to Production. Continually move from development environments to production

DataOps Speed ideas to production.

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.

dataops datakitchen platfrom"

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.

Main Features:

  • Orchestrate complex data pipelines
  • Create development sandboxes
  • Deploy quickly from development to production
  • Test and monitor quality automatically

Click Here To Learn More

Learn From The Best: Our Customers

Case Study: Celgene's Product Launch

Celgene. When a pharmaceutical company wins FDA approval, they enter a critical time. Celgene's analytic team's internal customers need up-to-date information to allocate samples, plan marketing events, and of course, monitor progress vs. goals. Changes happen at a frenetic pace and the tolerance for error is very low. The DataKitchen DataOps platform plays a key role in delivering insight fast, with high quality using a diverse set of tools. DataKitchen Recipes run daily incorporating the newest sales data from Veeva, corporate MDM data, and multiple syndicated data sources into a large Redshift database. Crack analysts use Tableau and Alteryx to present dashboards and develop predictive models. Read the Case Study and Blog Post

Case Study: Helping 130 Million People

CRS When one of the largest international relief organizations wants to understand their donors and increase the funds able to help 130 million people in more than 90 countries and territories, they know that the traditional slow and fragmented analytic process does not meet their needs. The analytic team needs an integrated database updated daily, the ability to make changes to that database weekly, and the confidence to use Tableau to make changes real-time. They need DataKitchen's DataOps platform to provide the mechanism to make quick changes, keep quality high, and use the tools they love.

Analyst vetted. Customer approved.

Learn More About DataKitchen's Leadership in DataOps from leading thinkers.

The DataOps Manifesto. Join The Revolution

18 Points That Will Rock Your World. Join the Thousands of People Who Have Already Signed The DataOps Manifesto.

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DataKitchen Headquarters:

101 Main Street, 14th Floor
Cambridge, MA 02142

Our Mailing Address:

One Broadway, 14th Floor
Cambridge, MA 02142