Top data engineering writer on Medium & Senior Data Engineer in media; I use my skills as a former journalist to demystify data science/programming concepts so beginners to professionals can target, land and excel in data-driven roles.
Can Your Data Pipelines Be Dangerous?
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 |
Pipeline To DE
Top data engineering writer on Medium & Senior Data Engineer in media; I use my skills as a former journalist to demystify data science/programming concepts so beginners to professionals can target, land and excel in data-driven roles.
Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! When you apply to data analysis, data engineering or data science jobs, you likely consider factors like company name, culture and compensation. Caught up in the excitement of a fresh opportunity or compelling offer you’re neglecting an important part of your day-to-day reality in a new role: What stage of data maturity the organization is in. If you’re looking for experience building something...
Extract. Transform. Read. A newsletter from Pipeline Sponsored by Basejump AI. Don't just query your data; chat with it. Learn more. Hi past, present or future data professional! If you’re a hybrid or remote worker (or online student) you know the FOMO that sets in when you leave your desk and something at work needs your immediate attention. Even though data engineering is a role that can be done from anywhere, having to open, set up and position a laptop isn’t ideal in all situations like...
Extract. Transform. Read. A newsletter from Pipeline: Your Data Engineering Resource Presented by Basejump AI Use natural language prompts to chat with your database in Basejump’s intuitive interface, or embed it directly in your application. Book your demo here. Hi past, present or future data professional! One thing that makes my work day easier is when I’m Google-ing (as all software developers do) a problem and I come across the holy grail of solutions: A one-line implementation. Like...