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We’ve likely been here: Table A has 56 columns and we want to select all but one of them (column_56). So here we go, let’s get started…

select
    column_1,
    column_2,
    column_3,
    please_save_me…
from {{ ref('table_a') }}

At this point, you realize your will to continue typing out the next 52 columns has essentially dwindled down to nothing and you’re probably questioning the life choices that led you here.

But what if there was a way to make these 56+ lines of code come down to a handful? Well, that’s where a handy dbt macro comes into play.

If you're reading this article, it looks like you're wondering how you can better optimize your Redshift queries - and you're probably wondering how you can do that in conjunction with dbt.

In order to properly optimize, we need to understand why we might be seeing issues with our performance and how we can fix these with dbt sort and dist configurations.

Analytics engineers (AEs) are constantly navigating through the names of the models in their project, so naming is important for maintainability in your project in the way you access it and work within it. By default, dbt will use your model file name as the view or table name in the database. But this means the name has a life outside of dbt and supports the many end users who will potentially never know about dbt and where this data came from, but still access the database objects in the database or business intelligence (BI) tool.

Model naming conventions are usually made by AEs, for AEs. While that’s useful for maintainability, it leaves out the people who model naming is supposed to primarily benefit: the end users. Good model naming conventions should be created with one thing in mind: Assume your end-user will have no other context than the model name. Folders, schema, and documentation can add additional context, but they may not always be present. Your model names will always be shown in the database.

There are so many different date functions in SQL—you have DATEDIFF, DATEADD, DATE_PART, and DATE_TRUNC to name a few. They all have their different use cases and understanding how and when they should be used is a SQL fundamental to get down. Are any of those as easy to use as the EXTRACT function? Well, that debate is for another time…

In this post, we’re going to give a deep dive into the EXTRACT function, how it works, and why we use it.

Hey data champion — so glad you’re here! Sometimes datasets need a team of engineers to tackle their deduplification (totz a real word), and that’s why we wrote this down. For you, friend, we wrote it down for you. You’re welcome!

Let’s get rid of these dupes and send you on your way to do the rest of the super-fun-analytics-engineering that you want to be doing, on top of super-sparkly-clean data. But first, let’s make sure we’re all on the same page.

We’ve all been there:

• In a user signup form, user A typed in their name as Kira Furuichi, user B typed it in as john blust, and user C wrote DAvid KrevitT (what’s up with that, David??)
• Your backend application engineers are adamant customer emails are in all caps
• All of your event tracking names are lowercase

In the real world of human imperfection, opinions, and error, string values are likely to take inconsistent capitalization across different data sources (or even within the same data source). There’s always a little lack of rhyme or reason for why some values are passed as upper or lowercase, and it’s not worth the headache to unpack that.

So how do you create uniformity for string values that you collect across all your data sources? The LOWER function!

❓ Who is this for: This is for advanced users of dbt Cloud that are interested in expanding their knowledge of the dbt API via an interactive Postman Collection. We only suggest diving into this once you have a strong knowledge of dbt + dbt Cloud. You have a couple of options to review the collection:

• get a live version of the collection via .
• check out the collection documentation to learn how to use it.

The dbt Cloud API has well-documented endpoints for creating, triggering and managing dbt Cloud jobs. But there are other endpoints that aren’t well documented yet, and they’re extremely useful for end-users. These endpoints exposed by the API enable organizations not only to orchestrate jobs, but to manage their dbt Cloud accounts programmatically. This creates some really interesting capabilities for organizations to scale their dbt Cloud implementations.

The main goal of this article is to spread awareness of these endpoints as the docs are being built & show you how to use them.

It’s inevitable in the field of analytics engineering: you’re going to encounter moments when there’s mysterious or unhelpful blank values in your data. Null values surely have their time and place, but when you need those null values filled with more meaningful data, COALESCE comes to the rescue.

COALESCE is an incredibly useful function that allows you to fill in unhelpful blank values that may show up in your data. In the words of analytics engineer Lauren Benezra, you will probably almost never see a data model that doesn’t use COALESCE somewhere.

Continuous Integration (CI) sets the system up to test everyone’s pull request before merging. Continuous Deployment (CD) deploys each approved change to production. “Slim CI” refers to running/testing only the changed code, thereby saving compute. In summary, CI/CD automates dbt pipeline testing and deployment.

dbt Cloud, a much beloved method of dbt deployment, supports GitHub- and Gitlab-based CI/CD out of the box. It doesn’t support Bitbucket, AWS CodeCommit/CodeDeploy, or any number of other services, but you need not give up hope even if you are tethered to an unsupported platform.

Although this article uses Bitbucket Pipelines as the compute service and Bitbucket Downloads as the storage service, this article should serve as a blueprint for creating a dbt-based Slim CI/CD anywhere. The idea is always the same:

dbt Cloud is a hosted service that many organizations use for their dbt deployments. Among other things, it provides an interface for creating and managing deployment jobs. When triggered (e.g., cron schedule, API trigger), the jobs generate various artifacts that contain valuable metadata related to the dbt project and the run results.

dbt Cloud provides a REST API for managing jobs, run artifacts and other dbt Cloud resources. Data/analytics engineers would often write custom scripts for issuing automated calls to the API using tools cURL or Python Requests. In some cases, the engineers would go on and copy/rewrite them between projects that need to interact with the API. Now, they have a bunch of scripts on their hands that they need to maintain and develop further if business requirements change. If only there was a dedicated tool for interacting with the dbt Cloud API that abstracts away the complexities of the API calls behind an easy-to-use interface… Oh wait, there is: the dbt-cloud-cli!

"I forgot to mention we dropped that column and created a new one for it!”

“Hmm, I’m actually not super sure why customer_id is passed as an int and not a string.”

“The primary key for that table is actually the order_id, not the id field.”

I think many analytics engineers, including myself, have been on the receiving end of some of these comments from their backend application developers.

Backend developers work incredibly hard. They create the database and tables that drive the heart of many businesses. In their efforts, they can sometimes overlook, forget, or not understand their impact on analytics work. However, when backend developers do understand and implement the technical and logistical requirements from data teams, they can spark joy.

So what makes strong collaboration possible between analytics engineers and backend application developers?

Special Thanks: Emilie Schario, Matt Winkler

dbt has done a great job of building an elegant, common interface between data engineers, analytics engineers, and any data-y role, by uniting our work on SQL. This unification of tools and workflows creates interoperability between what would normally be distinct teams within the data organization.

I like to call this interoperability a “baton pass.” Like in a relay race, there are clear handoff points & explicit ownership at all stages of the process. But there’s one baton pass that’s still relatively painful and undefined: the handoff between machine learning (ML) engineers and analytics engineers.

In my experience, the initial collaboration workflow between ML engineering & analytics engineering starts off strong but eventually becomes muddy during the maintenance phase. This eventually leads to projects becoming unusable and forgotten.

In this article, we’ll explore a real-life baton pass between ML engineering and analytics engineering and highlighting where things went wrong.

Executive Summary:

If your company is struggling to leverage analytics, dealing with an overgrown ecosystem of dashboards/databases or simply want to avoid the mistakes of others, this story is for you. In this article, I will walk through forming the first analytics engineering team at Smartsheet including how momentum built around forming the team,  the challenges we faced, and the solutions we developed within the first year.

Introduction​

Most writing about analytics engineering, or AE for short, assumes a team already exists. It’s about operating as an AE team or managing stakeholders or leveraging tools more effectively. But what about the prologue? What initial problems do AEs solve? How does an AE team even start? What do the early days look like?

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.

[We would love to have] A maturity curve of an end-to-end dbt implementation for each version of dbt .... There are so many features in dbt now but it'd be great to understand, "what is the minimum set of dbt features/components that need to go into a base-level dbt implementation?...and then what are the things that are extra credit?" -Will Weld on dbt Community Slack

One question we hear time and time again is this - what does it look like to progress through the different stages of maturity on a dbt project?

When Will posed this question on Slack, it got me thinking about what it would take to create a framework for dbt project maturity.

Having a GitHub pull request template is one of the most important and frequently overlooked aspects of creating an efficient and scalable dbt-centric analytics workflow. Opening a pull request is the final step of your modeling process - a process which typically involves a lot of complex work!

For you, the dbt developer, the pull request (PR for short) serves as a final checkpoint in your modeling process, ensuring that no key elements are missing from your code or project.

Without a command to run them, dbt models and tests are just taking up space in a Git repo.

The specific dbt commands you run in production are the control center for your project. They are the structure that defines your team’s data quality + freshness standards.

We’ve all done it: fanned out data during a join to produce duplicate records (sometimes duplicated in multiple).

That time when historical revenue numbers doubled on Monday? Classic fanout.

Could it have been avoided? Yes, very simply: by defining the uniqueness grain for a table with a primary key and enforcing it with a dbt test.

So let’s dive deep into: what primary keys are, which cloud analytics warehouses support them, and how you can test them in your warehouse to enforce uniqueness.

Why primary keys are important​

We all know one of the most fundamental rules in data is that every table should have a primary key. Primary keys are critical for many reasons:

• They ensure that you don’t have duplicate rows in your table
• They help establish relationships to other tables
• They allow you to quickly identify the grain of the table (ex: the customers table with a PK of customer_id has one row per customer)
• You can test them in dbt, to ensure that your data is complete and unique