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“DevOps,” “SecOps,” “DevSecOps,” “ChatOps,” “NoOps” — the terms go on and on. In this episode of InApps Analysts podcast, Toph Whitmore, principal analyst for Blue Hill Research, talks about data operations, or “DataOps.” He describes it as looking at the data production pipeline in a holistic manner, marrying data-management objectives with data-consumption ideals to maximize data-derived value.

DataOps and DevOps both require coordination between multiple teams. Above is a diagram that describes the data production value chain. Connoisseurs of DevOps/CI/CD pipelines, feel free to do a compare-and-contrast with your favorite model while listening in to our podcast:


#129: What is ‘DataOps’ and Why It Matters

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Show Notes

Blue Hill’s report, “DataOps: The Collaborative Framework for Enterprise Data-Flow Orchestration.”

Whitmore is excited about two data companies that rely on open source: Rocana and Talend.

To get a more technical explanation of DataOps, Whitmore recommends attending DataOps Summit 2017, June 20-21, Boston.

April 2016 Blue Hill podcast with Blue Hill’s James Haight, TNS’ Alex Williams and Benjamin Ball: Hadooponomics: The Game Theory of Open Source Foundations and the Big Data Skills Gap.

The ebook “Data Engineering Team: Creating Successful Big Data Teams and Products,” by Jesse Anderson.

Feature image: A conceptual assembly-line context diagram of “DataOps” with enterprise data value chain and horizontal data-governance-stack overlays. Source: Blue Hill Research.

Read More:   6 Kubernetes Best Practices to Empower Devs to Troubleshoot – InApps Technology 2022

Source: InApps.net

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