The legacy data quality tools were never designed (or intended) to serve as quality control tools for today’s complex continuous data pipelines that carry data in motion from application to ...

Developing applications rapidly utilizing DevOps requires data discipline. From integrating and managing data attributes using data repositories to tracking data from its source to its final ...

This week in DevOps shows big data and and DevOps are two of the latest IT trends to intersect–and just as with BI and big data, they unlock new potential for organizations when leveraged together.

Devops requirements for data scientists differ from application developers Not every organization may be ready to invest in data science platforms, or it may have small data science teams who only ...

Analytics, no matter how sophisticated, needs to be seen not as a project with an end, but something that is an integral part of the framework of the entire operation.

For this article, I consulted with industry experts to identify what devops leaders and teams should know about data governance and how they can contribute to its goals.

Ask any CTO or CIO about DevOps and data science, and they’ll say that smart enterprises are investing in expertise for both skill sets. The DevOps approach has made IT more responsive to business ...

New data-centric considerations have motivated the need for a practice that can transcend the limitations of DevOps.

ESM data can help enterprise DevOps teams gain the same collaborative agility and responsiveness as startup teams. But only if done right.

Ash Munshi, Pepperdata CEO, recently discussed the need for DevOps for big data, and the role of the Dr. Elephant project, which was open sourced in 2016 by LinkedIn and is available under the Apache ...