Extract. Transform. Read.A newsletter from Pipeline: Your Data Engineering ResourceHi past, present and future data professional! Since today is a U.S. holiday, I won’t take much of your time; the good news is that, when conducted efficiently, building a data pipeline doesn’t have to take days, weeks or months. In fact, you can build a data pipeline in as little as 90 minutes. Accelerating pipeline development depends on a thorough read of the documentation, a familiarity with your scripting language’s requests library and patience dealing with pesky data structures. If you think, during this time, engineers are heads-down, you may have watched The Social Network too many times; personally, I like a little external stimuli while coding, which is how I ended up building a full dashboard during another American pastime–a baseball game. My secret? Distilling data with clean views, which I recommend over bloated source tables for both aesthetic and performance reasons. Even optimizations like views have their limitations, leading to optimization ceilings. The best way to break through, aside from stubbornness, is a combination of incremental problem-solving and “big picture” data modeling to reassess resources and attack the problem completely. Since I don’t want you to have to work any harder today, here are the embedded links as text:
If you’re celebrating America today, happy 4th! 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,...