Editor's note: In this tutorial, Donny walks through the fictional story of a SaaS company called JaffleGaggle, who needs to group their freemium individual users into company accounts (aka a customer 360 view) in order to drive their product-led growth efforts.

You can follow along with Donny's data modeling technique for identity resolution in this dbt project repo. It includes a set of demo CSV files, which you can use as dbt seeds to test Donny's project for yourself.

Measuring the number of business hours between two dates using SQL is one of those classic problems that sounds simple yet has plagued analysts since time immemorial.

This comes up in a couple places at dbt Labs:

• Calculating the time it takes for a support ticket to be solved
• Measuring team performance against response time SLAs

We internally refer to this at "Time on Task," and it can be a critical data point for customer or client facing teams. Thankfully our tools for calculating Time on Task have improved just a little bit since 2006.

Even still, you've got to do some pretty gnarly SQL or dbt gymnastics to get this right, including:

1. Figuring out how to exclude nights and weekends from your SQL calculations
2. Accounting for holidays using a custom holiday calendar
3. Accommodating for changes in business hour schedules

This piece will provide an overview of how and critically why to calculate Time on Task and how we use it here at dbt Labs.

Doing analytics is hard. Doing analytics right is even harder.

There are a massive number of factors to consider: Is data missing? How do we make this insight discoverable? Why is my database locked? Are we even asking the right questions?

Compounding this is the fact that analytics can sometimes feel like a lonely pursuit.

Sure, our data is generally proprietary and therefore we can’t talk much about it. But we certainly can share what we’ve learned about working with that data.

So let’s all commit to sharing our hard won knowledge with each other—and in doing so pave the path for the next generations of analytics practitioners.

Most organizations spend at least some of their time contributing to an open source project. 100% of them, though, depend in some way on the output of open source communities.

If you’ve spoken to me lately, follow me on Twitter, or have taken my order at Wendy’s, you probably know how much I hate traditional dashboards. My dad, a psychotherapist, has been working with me to get to the root of my upbringing that led to this deep-rooted feeling.

💾 This article is for anyone who has ever questioned the sanity of a date not in ISO 8601 format

Have you ever been assigned to add new fields or concepts to an existing set of models and wondered:

• Why are there multiple models named almost the same but slightly different?

• Which model has the fields I need?

• Which model is upstream or downstream from which?