Summary The dream of every engineer is to automate all of their tasks. For data engineers, this is a monumental undertaking. Orchestration engines are one step in that direction, but they are not a complete solution. In this episode Sean Knapp shares his views on what constitutes proper automation and the work that he and his team at Ascend are doing to help make it a reality.

Announcements

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Interview

Introduction How did you get involved in the area of data management? Can you describe what you mean by the term "data automation" and the assumptions that it includes? One of the perennial challenges of automation is that there are always steps that are resistant to being performed without human involvement. What are some of the tasks that you have found to be common problems in that sense? What are the different concerns that need to be included in a stack that supports fully automated data workflows? There was recently an interesting article suggesting that the "left-to-right" approach to data workflows is backwards. In your experience, what would be required to allow for triggering data processes based on the needs of the data consumers? (e.g. "make sure that this BI dashboard is up to date every 6 hours") What are the