Defining DataOps with Chris Bergh - Episode 26

Episode • Apr 8, 2018 • 54m

Summary

Managing an analytics project can be difficult due to the number of systems involved and the need to ensure that new information can be delivered quickly and reliably. That challenge can be met by adopting practices and principles from lean manufacturing and agile software development, and the cross-functional collaboration, feedback loops, and focus on automation in the DevOps movement. In this episode Christopher Bergh discusses ways that you can start adding reliability and speed to your workflow to deliver results with confidence and consistency.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I’m interviewing Christopher Bergh about DataKitchen and the rise of DataOps

Interview

Introduction
How did you get involved in the area of data management?
How do you define DataOps?
- How does it compare to the practices encouraged by the DevOps movement?
- How does it relate to or influence the role of a data engineer?

How does a DataOps oriented workflow differ from other existing approaches for building data platforms?

One of the aspects of DataOps that you call out is the practice of providing multiple environments to provide a platform for testing the various aspects of the analytics workflow in a non-production context. What are some of the techniques that are available for managing data in appropriate volumes across those deployments?

The practice of testing logic as code is fairly well understood and has a large set of existing tools. What have you found to be some of the most effective methods for testing data as it flows through a system?

One of the practices of DevOps is to create feedback loops that can be used to ensure that business needs are being met. What are the metrics that you track in your platform to define the value that is being created and how the various steps in the workflow are proceeding toward that goal?
- In order to keep feedback loops fast it is necessary for tests to run quickly. How do you balance the need for larger quantities of data to be used for verifying scalability/performance against optimizing for cost and speed in non-production environments?

How does the DataKitchen platform simplify the process of operationalizing a data analytics workflow?

As the need for rapid iteration and deployment of systems to capture, store, process, and analyze data becomes more prevalent how do you foresee that feeding back into the ways that the landscape of data tools are designed and developed?

Contact Info

Parting Question

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

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