An Exploration Of What Data Automation Can Provide To Data Engineers And Ascend's Journey To Make It A Reality
Data Engineering Podcast
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Graphs and Data Flows - The Next Abstraction Layer
A clean architecture on as the implementation of new connectors very, very simple and easy to do. Unso we also create that these architecture so you can plug into everything. The next abstraction layer that we also created inside of a send is the ability to control your entire graphs and your data flows,. You can even dowmod them as executable python that you can then re apply back up to the a p is. And they wrap the s t k to go back and recreate these data flows until it is a by directional code level of zink into the system as well.
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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
- 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 Sean Knapp about the role of data automation in building maintainable systems
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 tasks that are most complex to build automation for?
- What are some companies or tools/platforms that you consider to be exemplars of "data automation done right"?
- What are the common themes/patterns that they build from?
- How have you approached the need for data automation in the implementation of the Ascend product?
- How have the requirements for data automation changed as data plays a more prominent role in a growing number of businesses?
- What are the foundational elements that are unchanging?
- What are the most interesting, innovative, or unexpected ways that you have seen data automation implemented?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on data automation at Ascend?
- What are some of the ways that data automation can go wrong?
- What are you keeping an eye on across the data ecosystem?
Contact Info
- @seanknapp on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
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Links
- Ascend
- Google Sawzall
- CI/CD
- Airflow
- Kubernetes
- Ascend FlexCode
- MongoDB
- SHA == Secure Hash Algorithm
- dbt
- Materialized View
- Great Expectations
- Monte Carlo
- OpenLineage
- Open Metadata
- Egeria
- OOM == Out Of Memory Manager
- Five Whys
- Data Mesh
- Data Fabric
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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