Automating Your Production Dataflows On Spark
November 4th, 2019
48 mins 50 secs
About this Episode
As data engineers the health of our pipelines is our highest priority. Unfortunately, there are countless ways that our dataflows can break or degrade that have nothing to do with the business logic or data transformations that we write and maintain. Sean Knapp founded Ascend to address the operational challenges of running a production grade and scalable Spark infrastructure, allowing data engineers to focus on the problems that power their business. In this episode he explains the technical implementation of the Ascend platform, the challenges that he has faced in the process, and how you can use it to simplify your dataflow automation. This is a great conversation to get an understanding of all of the incidental engineering that is necessary to make your data reliable.
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- Your host is Tobias Macey and today I’m interviewing Sean Knapp about Ascend, which he is billing as an autonomous dataflow service
- How did you get involved in the area of data management?
- Can you start by explaining what the Ascend platform is?
- What was your inspiration for creating it and what keeps you motivated?
- What was your criteria for determining the best execution substrate for the Ascend platform?
- Can you describe any limitations that are imposed by your selection of Spark as the processing engine?
- If you were to rewrite Spark from scratch today to fit your particular requirements, what would you change about it?
- Can you describe the technical implementation of Ascend?
- How has the system design evolved since you first began working on it?
- What are some of the assumptions that you had at the beginning of your work on Ascend that have been challenged or updated as a result of working with the technology and your customers?
- How does the programming interface for Ascend differ from that of a vanilla Spark deployment?
- What are the main benefits that a data engineer would get from using Ascend in place of running their own Spark deployment?
- How do you enforce the lack of side effects in the transforms that comprise the dataflow?
- Can you describe the pipeline orchestration system that you have built into Ascend and the benefits that it provides to data engineers?
- What are some of the most challenging aspects of building and launching Ascend that you have dealt with?
- What are some of the most interesting or unexpected lessons learned or edge cases that you have encountered?
- What are some of the capabilities that you are most proud of and which have gained the greatest adoption?
- What are some of the sharp edges that remain in the platform?
- When is Ascend the wrong choice?
- What do you have planned for the future of Ascend?
- @seanknapp on Twitter
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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- Apache Spark
- Apache Beam
- Go Language
- SHA Hashes
- Delta Lake
- DAG == Directed Acyclic Graph
- Snappy Compression
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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