[ETR #21] Data Pipeline Non-Negotiables


Extract. Transform. Read.

A newsletter from Pipeline

Hi past, present or future data professional!

Despite falling into the realm of engineering, data infrastructure construction is a bit like basic art. At times building a data pipeline is as simple as filling in one of those color-by-numbers books. Other times, the process of extracting and ingesting data can be as abstract and disconnected as paint flicked onto a canvas, Jackson Pollack style.

No matter the complexity of your build, there are always certain brushes, a.k.a. non-negotiables, you should paint with to create intuitive and robust pipelines. I consider the following recommendations to be non-negotiable because they serve the most basic goal of a data pipeline: Providing reliable, prompt and accurate data to data consumers.

A non-negotiable you must include in not only data pipelines, but programmatic scripts at large, is a clear, consistent and accessible form of logging. Good logs will concisely reflect what is going on within a script, revealing insights about each function or step as it is executed. Learn more about the importance of logging and best practices here.

Going hand-in-hand with logging is the capturing of and reference to API status codes. While not all APIs will emit similar text messages when a response is triggered, there are universal codes like 200 that can be helpful in indicating the presence of data or other attributes and distinguish an unsuccessful request from a successful effort.

Once you have the data, I’d suggest, as a non-negotiable, that you keep it in a consistent format. It might be nice being able to iterate through columns in a data frame, convert it to JSON, and then convert to a final data frame, but the resources required to execute the transformations and redundancy of the operations makes this inefficient. If you have to do significant work to unnest data, for instance, it may be better and more efficient to keep your data in JSON form.

Finally, one of the worst things a pipeline can do (after breaking) is generate duplicate data. Nearly every one of my work builds includes what I call a “refresh” query that deletes the current date’s data as the pipe runs. This means that if the pipeline has to run again, it will generate the exact same output. The word for maintaining state like this is “idempotent.” In an org running hundreds of pipelines, you don’t want to create the 1 pipe with an uncontrollable output.

To review, non-negotiables include:

  • Logging statements
  • A record of API status codes/output
  • Consistent data format
  • “Refresh” delete statements to make the pipeline idempotent

This week's links:

What did I miss? Reply to this email and let me know.

Thanks for ingesting,

-Zach Quinn

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.

Read more from Pipeline To DE

Extract. Transform. Read. A Newsletter From Pipeline Hi past, present or future data professional! Since today marks Thanksgiving in the US, I hope this reaches you before your eyes glaze over from the tryptophan-induced turkey coma we all inevitably slip into. While today is a day of gratitude, from a data engineering perspective, I’d like to focus, instead, on the under-the-radar tasks that can make a difference at this time of year—even if they don’t gain you any recognition at work. The...

Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! It’s never good when you wake up to this from a coworker: 💀 The skull wasn’t because the sender felt like they would suffer any kind of dramatic fate. Instead, they were prepared to administer near-fatal justice to the junior engineer who made several unnecessary overnight commits straight to our org’s main branch. The thing is, for a first-time violation, I can understand why testing is an...

Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! It’s been a busy fall; I currently have 14 tasks in various states of development. Right now my JIRA board looks like I just won bingo—twice. Unfortunately when you climb the tech ladder things only get busier which means you’re going to burn out unless you take steps toward proactivity. For me this means learning which tasks I don’t need to (and really shouldn’t) do manually. And before you...