DataOps | Develop & Deploy Data Analytic Pipelines

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

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 Want Continuously Change Production? Be Agile.

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

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.