A DataOps vs DevOps Cookoff In The Data Kitchen


March 18th, 2019

54 mins 31 secs

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About this Episode


Delivering a data analytics project on time and with accurate information is critical to the success of any business. DataOps is a set of practices to increase the probability of success by creating value early and often, and using feedback loops to keep your project on course. In this episode Chris Bergh, head chef of Data Kitchen, explains how DataOps differs from DevOps, how the industry has begun adopting DataOps, and how to adopt an agile approach to building your data platform.


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  • Your host is Tobias Macey and today I’m interviewing Chris Bergh about the current state of DataOps and why it’s more than just DevOps for data


  • Introduction
  • How did you get involved in the area of data management?
  • We talked last year about what DataOps is, but can you give a quick overview of how the industry has changed or updated the definition since then?
    • It is easy to draw parallels between DataOps and DevOps, can you provide some clarity as to how they are different?
  • How has the conversation around DataOps influenced the design decisions of platforms and system components that are targeting the "big data" and data analytics ecosystem?
  • One of the commonalities is the desire to use collaboration as a means of reducing silos in a business. In the data management space, those silos are often in the form of distinct storage systems, whether application databases, corporate file shares, CRM systems, etc. What are some techniques that are rooted in the principles of DataOps that can help unify those data systems?
  • Another shared principle is in the desire to create feedback cycles. How do those feedback loops manifest in the lifecycle of an analytics project?
  • Testing is critical to ensure the continued health and success of a data project. What are some of the current utilities that are available to data engineers for building and executing tests to cover the data lifecycle, from collection through to analysis and delivery?
  • What are some of the components of a data analytics lifecycle that are resistant to agile or iterative development?
  • With the continued rise in the use of machine learning in production, how does that change the requirements for delivery and maintenance of an analytics platform?
  • What are some of the trends that you are most excited for in the analytics and data platform space?

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Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?


The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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