All things Data Newsletter #14 (#dataengineering #datascience #data #analytics)


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The goal of this newsletter is to promote continuous learning for data science and engineering professionals. To achieve this goal, I’ll be sharing articles across various sources that I found interesting. The following 5 articles made the cut for today’s newsletter.

(1) Analytics is a mess

Fantastic article highligting the importance of the “creative” side of analytics. It’s not always structured and that is also what makes it fun. Read here

(2) Achieving metric consistency at Scale — Airbnb Data

This is a great case study shared by Airbnb’s data team on how they achived metrics consistency at Scale. Read here

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(3) Achieving metric consistency & standardization — Uber Data

Another great read on metrics standardization — this time at Uber. As you can notice it’s a recurring problem at different organizations after hitting a certain growth threshold. This problem occurs since in the intial growth stage, there’s a lot of focus on enabling folks to look at metrics in a manner that’s optimized for speed. After a certain stage, this needs to balanced with consistency where the teams might have gone in different direction and they are defining the same thing in different way but that doesn’t scale anymore since you need some consistency and standardization. This is where the topic of metric consistency and standardization comes in. It’s a problem worth solving — and if you are interested, please read this article here

(4) Where is data engineering going in 5 years?

A good short post by Zach Wilson on LinkedIn talking about where data engineering is going over the next few years. Not surprised to see Data privacy in there! Read others here

(5) 3 foundations of successful data science team

An Amazon leader (Elvis Dieguez) talks about the 3 foundational pillards of a successful data team. This is comprised of 1) data warehouse 2) automated data pipelines 3) self-service analytics tool. Read here

Thanks for reading! Now it’s your turn: Which article did you love the most and why?

What do you think? Leave a comment below.