The case for the ELT workflow

We had to draw a line in the sand, or we’d never make the switch. The time had come to move our data transformation pipeline to an ELT (extract -> load -> transform) workflow. Our old ETL (extract -> transform -> load) pipeline, a set of stored procedures running on a Postgres database (PL/pgSQL to […]

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Modular data modeling technique

Back when I started working in the data industry, as part of recruitment you’d get this Army-style pamphlet about all the cool stuff you’re going to do. Then you sit down at your desk, and things get messy. Now often in the world, the unhealthy things are kind of fun: like french fries and Coca-Cola, […]

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A love letter to ETL tools

I love anything that helps me get deep, anxiety-free, sleep. Dog-on-air-vent, cat-in-window kind of sleep. Back in September 2010, for my first job in analytics, I careened into the office at 6:30am every day, unsure of what issues might be awaiting me. My assignment was to sign off on risk analytics reporting for BlackRock’s banking […]

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Why does it exist?

In his 2016 post “Building a Mature Analytics Workflow,” our own Tristan Handy outlined the significant issues facing analytics teams, and how we might begin solving them. The crux of the issue was that data practitioners clean & transform their datasets in isolation, as part of building their own analytics outputs (reports, notebooks, ML models, […]

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What is analytics engineering?

Analytics engineering applies software engineering best practices to analytics code.

It’s a practice that allows anyone who knows SQL to produce excellent datasets — datasets that are fresh, accurate, tested, and documented.

Teams practice analytics engineering collaboratively — it is generally not a solo sport (unless you’re a data team of one!).

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