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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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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- Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos.
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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.
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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
Sponsored By:
- Bigeye:  Bigeye is an industry-leading data observability platform that gives data engineering and science teams the tools they need to ensure their data is always fresh, accurate and reliable. Companies like Instacart, Clubhouse, and Udacity use Bigeye’s automated data quality monitoring, ML-powered anomaly detection, and granular root cause analysis to proactively detect and resolve issues before they impact the business. Go to [dataengineeringpodcast.com/bigeye](https://www.dataengineeringpodcast.com/bigeye) today and start trusting your data.
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