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Joe Reis on data engineering and architecture
Manage episode 334201468 series 3370867
Joe Reis is a business-minded data engineer who’s worked in the industry for 20 years. He is the CEO and Co-Founder of Ternary Data, a data engineering and architecture consulting firm based in Salt Lake City, Utah. He volunteers with several technology groups and teaches at the University of Utah. In his spare time, he likes to rock climb, produce electronic music, and take his kids on crazy adventures.
In this episode, we cover a range of topics including:
- What does a data engineer do?
- Relationship between data engineering and MLOps
- What are the components of the data engineering lifecycle?
- What should a data engineer know at a fundamental level to be successful at this?
- His book Fundamentals of Data Engineering
- Why writing a book is one of the few activities that exposes your weaknesses and very deeply refines your thinking
- The three pillars of a solid data foundation: data architecture, data engineering, and DataOps.
- How do you structure your first call with a potential client?
- Why is there a mismatch between expectations and reality when it comes to using data science within a business?
- Why he puts a lot of the responsibility on the student to get good at researching problems and solutions
- How to think about architecture as a data professional
175 episodes
Manage episode 334201468 series 3370867
Joe Reis is a business-minded data engineer who’s worked in the industry for 20 years. He is the CEO and Co-Founder of Ternary Data, a data engineering and architecture consulting firm based in Salt Lake City, Utah. He volunteers with several technology groups and teaches at the University of Utah. In his spare time, he likes to rock climb, produce electronic music, and take his kids on crazy adventures.
In this episode, we cover a range of topics including:
- What does a data engineer do?
- Relationship between data engineering and MLOps
- What are the components of the data engineering lifecycle?
- What should a data engineer know at a fundamental level to be successful at this?
- His book Fundamentals of Data Engineering
- Why writing a book is one of the few activities that exposes your weaknesses and very deeply refines your thinking
- The three pillars of a solid data foundation: data architecture, data engineering, and DataOps.
- How do you structure your first call with a potential client?
- Why is there a mismatch between expectations and reality when it comes to using data science within a business?
- Why he puts a lot of the responsibility on the student to get good at researching problems and solutions
- How to think about architecture as a data professional
175 episodes
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