June 2021
Intermediate to advanced
264 pages
7h 19m
English
Content preview from 97 Things Every Data Engineer Should Know
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.
Chapter 60. Seven Things Data Engineers Need to Watch Out for in ML Projects
Dr. Sandeep Uttamchandani
According to a recent estimate, 87% of machine learning (ML) projects fail!1 This chapter covers the top seven things I have seen go wrong in an ML project from a data-engineering standpoint. The list is sorted in descending order based on the number of times I have encountered the issue multiplied by the impact of the occurrence on the overall project:
I thought this dataset attribute meant something else. Prior to the big data era, data was curated before being added to the central data warehouse. This is known as schema-on-write. Today, the approach with data lakes is to first aggregate the data and then infer its meaning at the time of consumption. This is known as schema-on-read. As a data engineer, be wary of using datasets without proper documentation of attribute details or a clear data steward responsible for keeping the details updated!
Five definitions exist for the same business metric—which should I use? Derived data or metrics can have multiple sources of truth! For instance, I have seen even basic metrics such as Count of New Customers having multiple definitions across business units. As a data engineer, if a business metric is being used in the model, be sure to look for all the available definitions and their corresponding ETL implementations.
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.