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!' Today I’m turning the newsletter over to my friend Ken Jee (writer of AI Survival Guide, creator of Newsletter Hero) to share how he cuts through the noise of shiny AI products to find tools that enhance technical work. My Simple Framework For Adopting AI Tools Ken Jee As new AI tools launch almost daily, a quiet tax is emerging. Decision fatigue. Every new model, agent, or workflow tool carries the same implicit question. Should I switch, or should I go deeper...
Hi fellow data professional! Quick question: How much could I pay you to switch your job? Conventional wisdom in the tech industry in the last handful of years is that the way to supercharge growth and max out your career earnings is to frequently change jobs. On average, job switchers could and should target an increase of 15-20% of their current salary. But in a rocky economy (at least here in the U.S.), career experts are urging would-be switchers to consider the benefits of a stable role...
Hi fellow data professional and Happy New Year! In the second half of 2025, I made a radical choice: I (largely) stopped blogging. Over the past year, Medium (where I host my content) made a series of changes that de-prioritizes technical content, leading to the departure of several major publications, including Toward Data Science. Pair that platform disillusionment with a bit of burnout, and the result is a feeling that it’s time for a change. For 75+ weeks, I’ve preferred concise,...