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.
[ETR #16] Better Data Solutions In 5 Steps
Extract. Transform. Read.A newsletter from PipelineHi 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 new from the ground up, you likely won’t find it in a company that has a years-old established cloud infrastructure. If you’re inexperienced, you might also feel lost in a company that is still conceptualizing how it is going to establish and scale its data infra. While I personally arrived at a team and organization in its mid-life stage, I’ve had opportunities to discuss, examine and advise those who are considering how they can make an impact at an earlier-stage company in both full-time and contract roles. This compelled me, after a transatlantic flight, to compile a framework you can use to conceptualize anything from an in-house data solution to full-fledged infrastructure. Phase 1 Discovery - Extensive, purposeful requirements gathering to make sure you are providing a solution and, more importantly, a service, to an end user. Phase 2 Design - You can’t begin a journey or a complex technical build without a road map; take time to make a wish list of must-have data sources and sketch your architecture before writing line 1 of code. Phase 3 Ingestion - Build your pipelines according to best practices with a keen eye on cost and consumption; expect this to take 6-12 months depending on your work situation. Phase 4 Downstream Build - Going hand-in-hand with requirements gathering, consider how your target audience will use what you’ve built; might it be better to simplify or aggregate data sources in something like a view? Phase 5 Quality Assurance And Ongoing Tasks - Even though your pipelines and dashboards will be automated initially, nothing in data engineering is 100% automated. Components will break. You’ll be expected to fix them. And assure it doesn’t happen again. These 5 phases aren’t meant to be strict rules for building data infra. But they should get you thinking about how to build something purposefully so you can spend your time dealing with angry code–not stakeholders. Dive into the framework here. Here are this week’s links:
Until next time–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.
Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! From 2014-2017 I lived in Phoenix, Arizona and enjoyed the state’s best resident privilege: No daylight saving time. If you’re unaware (and if you're in the other 49 US states, you’re really unaware), March 9th was daylight saving, when we spring forward an hour. If you think this messes up your microwave and oven clocks, just wait until you check on your data pipelines. Even though data teams...
Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! As difficult as data engineering can be, 95% of the time there is a structure to data that originates from external streams, APIs and vendor file deliveries. Useful context is provided via documentation and stakeholder requirements. And specific libraries and SDKs exist to help speed up the pipeline build process. But what about the other 5% of the time when requirements might be structured, but...
Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! To clarify the focus of this edition of the newsletter, the reason you shouldn’t bother learning certain data engineering skills is due to one of two scenarios— You won’t need them You’ll learn them on the job You won’t need them Generally these are peripheral skills that you *technically* need but will hardly ever use. One of the most obvious skills, for most data engineering teams, is any...