Writing The Book That Offers A Single Reference For The Fundamentals Of Data Engineering
Data Engineering Podcast
The Biggest Gap in Data Observability
i'd like to get each of your perspectives on what you see as being the biggest gap in the tooling or technology that's available for data management. Caus iges said, right now it seems like that knowledge is spread across whatever random blog post you happen to be reading. I think we need better tools for almost like border line machine learning, to say, hey, this data looks really weird. Is it because we had a recession or cobit? Or is it because the data is bad? And start being able to raise alarms when we see weird data.
Summary
Data engineering is a difficult job, requiring a large number of skills that often don’t overlap. Any effort to understand how to start a career in the role has required stitching together information from a multitude of resources that might not all agree with each other. In order to provide a single reference for anyone tasked with data engineering responsibilities Joe Reis and Matt Housley took it upon themselves to write the book "Fundamentals of Data Engineering". In this episode they share their experiences researching and distilling the lessons that will be useful to data engineers now and into the future, without being tied to any specific technologies that may fade from fashion.
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 Joe Reis and Matt Housley about their new book on the Fundamentals of Data Engineering
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you explain what possessed you to write such an ambitious book?
- What are your goals with this book?
- What was your process for determining what subject areas to include in the book?
- How did you determine what level of granularity/detail to use for each subject area?
- Closely linked to what subjects are necessary to be effective as a data engineer is the concept of what that title encompasses. How have the definitions shifted over the past few decades?
- In your experiences working in industry and researching for the book, what is the prevailing view on what data engineers do?
- In the book you focus on what you term the "data lifecycle engineer". What are the skills and background that are needed to be successful in that role?
- Any discussion of technological concepts and how to build systems tends to drift toward specific tools. How did you balance the need to be agnostic to specific technologies while providing relevant and relatable examples?
- What are the aspects of the book that you anticipate needing to revisit over the next 2 – 5 years?
- Which elements do you think will remain evergreen?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on writing "Fundamentals of Data Engineering"?
- What are your predictions for the future of data engineering?
Contact Info
- Joe
- Matt
- @doctorhousley on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Fundamentals of Data Engineering (affiliate link)
- Ternary Data
- Designing Data Intensive Applications
- James Webb Space Telescope
- Google Colossus Storage System
- DMBoK == Data Management Body of Knowledge
- DAMA
- Bill Inmon
- Apache Druid
- RTFM == Read The Fine Manual
- DuckDB
- VisiCalc
- Ternary Data Newsletter
- Meroxa
- Ruby on Rails
- Lambda Architecture
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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