Extract. Transform. Read.A newsletter from Pipeline: Your Data Engineering ResourceHi past, present or future data professional! Data engineering can be dangerous; ok—not, like, physically, but by building and maintaining data infrastructure, data engineers are given a surprising amount of access and responsibility. Every commit, table alteration and deletion must be made with care. It took 2 years, but I finally learned a shortcut to make developing SQL staging tables less risky and more efficient. Even seemingly minor mistakes like joining on the wrong key can result in losing days or months of valuable data, which can be equal to hundreds of thousands or millions of dollars in revenue visibility. Outside of code mistakes, not paying attention to logistic factors like vendor contracts and API usage can not only result in downtime, in a worst-case scenario it can lead to an all-out blackout. If the stakes sound ominous, I’d suggest examining the root of your hesitation to work more confidently and efficiently—it may even be the code itself. There is a happy medium between freely building data pipelines and using the appropriate guard rails. As long as you take your time and don’t commit code directly to the main branch then you can do data engineering safely and avoid bursting your pipelines. For those who are anti-virus minded, here are this week’s links as plain text:
P.S. Want to learn how to go from code to automated pipeline? Take advantage of my 100% free email course: Deploy Google Cloud Functions In 5 Days. Thanks for ingesting, -Zach |
Reaching 20k+ readers on Medium and over 3k learners by email, I draw on my 4 years of experience as a Senior Data Engineer to demystify data science, cloud and programming concepts while sharing job hunt strategies so you can land and excel in data-driven roles. Subscribe for 500 words of actionable advice every Thursday.
Hi fellow data professional! SQL Lite, the database you most likely learned SQL on, is built atop 100k lines of source code. Sound like a lot? Compare that to Chromium, the engine for Google Chrome, which boasts 30+ million lines of code under the hood. Shortly after acquiring Twitter/X, the world's first trillionaire, Elon Musk, famously asked engineers to tell him how many lines of code they wrote per day, igniting a debate among engineers throughout the software and data domains. When I...
Hi fellow data professional! If you read my note on Tuesday you’ll know I’m coming off of the data engineering week from hell that seeped into my personal life, and delayed the launch of something cool I was planning to share with you; if you want to know more about that, scroll to the end of this message. Last week a flagship data source had a major problem and since it’s within my ownership area, I was the one with the knowledge and responsibility to fix it. I wanted to share the experience...
Hi fellow data professional! Hardly a work day goes by without receiving a request from a data analyst. They range from the mundane “Can you add this column?” to the occasional emergency “The data didn’t load all weekend and the leadership call starts in 15 minutes!” At the end of a jam-packed week I received an unusual request: Help with a Python script. My teammate wanted to know: Best practices How to commit to GitHub What the best way to deploy is They admitted the task was simple,...