How-To

A startup's guide to modeling data following Kimball best practices

This article is written for job seekers. I’ve gathered a lot of data over the course of a month-long search and learned about what the landscape looks like for the analytics engineer hiring pipeline.

A holistic data testing framework to help you catch data quality issues before they impact your stakeholders.

Marketing attribution has long been one of the stickiest problems in analytics. But with raw data, SQL, and dbt a previously complex problem can become beautifully simple.

If you’re a data analyst at a SaaS company or an ecommerce business with a subscription component, it is inevitable that you will be asked to analyze metrics like churn, upgrades, and downgrades. Here's how to do this with dbt.

Analytics code is code. Which means that it will have bugs. And without rigorous processes in place to find and eliminate those bugs, code quality will degrade over time.

Data teams are successful when they build reserves of trust. One important way to build trust is by consistently delivering projects on time. Here are four questions we've found to be useful in helping us spot the potholes that derail analytics engineering projects.

All data analysts have faced the problem–how do I model data when the record in the data warehouse keeps changing? In this post, I'll cover how to solve this using dbt snapshots.

Version control is a critical part of the software engineering workflow, but for many analysts, writing and reviewing pull requests is still a relatively new habit. Here's a checklist we use to review analytics PRs.

Advice from analysts and data engineers at HubSpot, Managed by Q, GitLab, Wong Decision Intelligence, AskWonder, and Calogica

Stephen Whitworth, Data Engineering Lead at Monzo, spoke at the London dbt Meetup a few weeks ago. Here are my thoughts on his talk and what the dbt community can learn from it.

Unnesting arrays on Redshift is a pain, but it’s doable. This post walks through the entire process, start to finish.

At Fishtown Analytics, we've set up Snowflake accounts for several dozen clients and have an approach we like quite a lot.

If you’re a hotshot junior analyst and you want a guide for breaking through this skills plateau, this guide is for you.

This is a very stupid problem. I am not writing this post because it’s a fascinating topic—rather, I’m writing it in the hopes that you avoid the headaches I've gone through.

I got this question on my last post. It is a good one, and one that I’ve gotten a bunch of times over the past year and a half. This post is my attempt to answer it.

dbt and Looker were built with the same ethos: analysts should be working in code.

Frequently asked questions about Looker explores and how to design them very well.

Want to go from syncing new data to having transformed data models built in minutes? Packages are going to be your friend.

Over the past nine months I’ve worked with over a dozen venture-funded startups to build out their internal analytics. In doing so, there’s a single SQL function that I have come to use surprisingly often.