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Summary
Data Engineering is a broad and constantly evolving topic, which makes it difficult to teach in a concise and effective manner. Despite that, Daniel Molnar and Peter Fabian started the Pipeline Academy to do exactly that. In this episode they reflect on the lessons that they learned while teaching the first cohort of their bootcamp how to be effective data engineers. By focusing on the fundamentals, and making everyone write code, they were able to build confidence and impart the importance of context for their students.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
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- Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
- Your host is Tobias Macey and today I’m interviewing Daniel Molnar and Peter Fabian about the lessons that they learned from their first cohort at the Pipeline data engineering academy
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you start by sharing the curriculum and learning goals for the students?
- How did you set a common baseline for all of the students to build from throughout the program?
- What was your process for determining the structure of the tasks and the tooling used?
- What were some of the topics/tools that the students had the most difficulty with?
- What topics/tools were the easiest to grasp?
- What are some difficulties that you encountered while trying to teach different concepts?
- How did you deal with the tension of teaching the fundamentals while tying them to toolchains that hiring managers are looking for?
- What are the successes that you had with this cohort and what changes are you making to your approach/curriculum to build on them?
- What are some of the failures that you encountered and what lessons have you taken from them?
- How did the pandemic impact your overall plan and execution of the initial cohort?
- What were the skills that you focused on for interview preparation?
- What level of ongoing support/engagement do you have with students once they complete the curriculum?
- What are the most interesting, innovative, or unexpected solutions that you saw from your students?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working with your first cohort?
- When is a bootcamp the wrong approach for skill development?
- What do you have planned for the future of the Pipeline Academy?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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