This guide is not a technical deep dive on any specific tool or technology.
It will not teach you how to pick a data visualization tool, nor how to assemble your version of the modern data stack.
Instead, let’s explore a thornier subject: how can we collaborate, as human data practitioners, to produce excellent datasets?
The practice of analytics engineering moves data along the journey from its rawest form (the transaction or event stream) to its end use (a report, an ML model, an operational workflow, a notebook, spreadsheet etc).
Along the way, numerous data people must come together: not just analytics engineers, but also data analysts, data engineers and data scientists.
We all converge around the shared goal of producing accurate, timely and understandable datasets.
So how does this workflow look in practice?
For each step in the analytics engineering workflow - from raw data extraction, to data modeling, to end uses like reporting or data science - let’s explore:
At the top of each section in this guide, we’ll offer a TL;DR summary of these 5 points, in case you’re looking to quickly scan.
Following this step-by-step, we’ll peer into data team structures, and how various teams have brought the right people together to build a successful analytics engineering practice.
To find your way around the guide, in the sidebar to the left (or in the hamburger menu on mobile), you’ll see these sections broken down piecewise:
You can always head back to the guide homepage for a detailed walkthrough of each of these sections.