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Analytics craft

The art of being an analytics practitioner.

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· 7 min read

Why we built this: A brief history of the dbt Labs Professional Services team

If you attended Coalesce 2022, you’ll know that the secret is out — the dbt Labs Professional Services team is not just a group of experienced data consultants; we’re also an intergalactic group of aliens traveling the Milky Way on a mission to enable analytics engineers to successfully adopt and manage dbt throughout the galaxy.

· 11 min read

Once your data warehouse is built out, the vast majority of your data will have come from other SaaS tools, internal databases, or customer data platforms (CDPs). But there’s another unsung hero of the analytics engineering toolkit: the humble spreadsheet.

Spreadsheets are the Swiss army knife of data processing. They can add extra context to otherwise inscrutable application identifiers, be the only source of truth for bespoke processes from other divisions of the business, or act as the translation layer between two otherwise incompatible tools.

Because of spreadsheets’ importance as the glue between many business processes, there are different tools to load them into your data warehouse and each one has its own pros and cons, depending on your specific use case.

· 6 min read

Data is an industry of sidesteppers. Most folks in the field stumble into it, look around, and if they like what they see, they’ll build a career here. This is particularly true in the analytics engineering space. Every AE I’ve talked to had envisioned themselves doing something different before finding this work in a moment of serendipity. This raises the question, how can someone become an analytics engineer intentionally? This is the question dbt Labs’ Foundry Program aims to address.

· 11 min read

When you were in grade school, did you ever play the “Telephone Game”? The first person would whisper a word to the second person, who would then whisper a word to the third person, and so on and so on. At the end of the line, the final person would loudly announce the word that they heard, and alas! It would have morphed into a new word completely incomprehensible from the original word. That’s how life feels without an analytics engineer on your team.

So let’s say that you have a business question, you have the raw data in your data warehouse, and you’ve got dbt up and running. You’re in the perfect position to get this curated dataset completed quickly! Or are you?

· 10 min read

Why do people cherry pick into upper branches?

The simplest branching strategy for making code changes to your dbt project repository is to have a single main branch with your production-level code. To update the main branch, a developer will:

  1. Create a new feature branch directly from the main branch
  2. Make changes on said feature branch
  3. Test locally
  4. When ready, open a pull request to merge their changes back into the main branch

Basic git workflow

If you are just getting started in dbt and deciding which branching strategy to use, this approach–often referred to as “continuous deployment” or “direct promotion”–is the way to go. It provides many benefits including:

  • Fast promotion process to get new changes into production
  • Simple branching strategy to manage

The main risk, however, is that your main branch can become susceptible to bugs that slip through the pull request approval process. In order to have more intensive testing and QA before merging code changes into production, some organizations may decide to create one or more branches between the feature branches and main.

· 10 min read

If you’ve ever heard of Marie Kondo, you’ll know she has an incredibly soothing and meditative method to tidying up physical spaces. Her KonMari Method is about categorizing, discarding unnecessary items, and building a sustainable system for keeping stuff.

As an analytics engineer at your company, doesn’t that last sentence describe your job perfectly?! I like to think of the practice of analytics engineering as applying the KonMari Method to data modeling. Our goal as Analytics Engineers is not only to organize and clean up data, but to design a sustainable and scalable transformation project that is easy to navigate, grow, and consume by downstream customers.

Let’s talk about how to apply the KonMari Method to a new migration project. Perhaps you’ve been tasked with unpacking the kitchen in your new house; AKA, you’re the engineer hired to move your legacy SQL queries into dbt and get everything working smoothly. That might mean you’re grabbing a query that is 1500 lines of SQL and reworking it into modular pieces. When you’re finished, you have a performant, scalable, easy-to-navigate data flow.

· 14 min read

Analyzing financial data is rarely ever “fun.” In particular, generating and analyzing financial statement data can be extremely difficult and leaves little room for error. If you've ever had the misfortune of having to generate financial reports for multiple systems, then you will understand how incredibly frustrating it is to reinvent the wheel each time.

This process can include a number of variations, but usually involves spending hours, days, or weeks working with Finance to:

  • Understand what needs to go into the reports
  • Model said reports
  • Validate said reports
  • Make adjustments within your model
  • Question your existence
  • Validate said reports again

You can imagine how extremely time consuming this process can be. Thankfully, you can leverage core accounting principles and other tools to more easily and effectively generate actionable financial reports. This way, you can spend more time diving into deeper financial analyses.

· 12 min read

Those who have been building data warehouses for a long time have undoubtedly encountered the challenge of building surrogate keys on their data models. Having a column that uniquely represents each entity helps ensure your data model is complete, does not contain duplicates, and able to join across different data models in your warehouse.

Sometimes, we are lucky enough to have data sources with these keys built right in — Shopify data synced via their API, for example, has easy-to-use keys on all the tables written to your warehouse. If this is not the case, or if you build a data model with a compound key (aka the data is unique across multiple dimensions), you will have to rely on some strategy for creating and maintaining these keys yourself. How can you do this with dbt? Let’s dive in.

· 15 min read

The larger a data ecosystem gets, the more its users and stakeholders expect consistency. As the ratio of data models to team members (to say nothing of stakeholders to team members) skyrockets, an agreed-upon modeling pattern often acts as scaffolding around that growth.

The biggest tool in the toolbox today, dimensional modeling, offers enough consistency to make it the dominant approach in the space, but what might be possible if we shut that toolbox, took a break from our workbench, and instead strolled over to our bookshelf?

In other words, what if we told a story?

· 15 min read

When running a job that has over 1,700 models, how do you know what a “good” runtime is? If the total process takes 3 hours, is that fantastic or terrible? While there are many possible answers depending on dataset size, complexity of modeling, and historical run times, the crux of the matter is normally “did you hit your SLAs”? However, in the cloud computing world where bills are based on usage, the question is really “did you hit your SLAs and stay within budget”?

Here at dbt Labs, we used the Model Timing tab in our internal analytics dbt project to help us identify inefficiencies in our incremental dbt Cloud job that eventually led to major financial savings, and a path forward for periodic improvement checks.

· 13 min read

At dbt Labs, we have best practices we like to follow for the development of dbt projects. One of them, for example, is that all models should have at least unique and not_null tests on their primary key. But how can we enforce rules like this?

That question becomes difficult to answer in large dbt projects. Developers might not follow the same conventions. They might not be aware of past decisions, and reviewing pull requests in git can become more complex. When dbt projects have hundreds of models, it's hard to know which models do not have any tests defined and aren't enforcing your conventions.

· 11 min read

Stored procedures are widely used throughout the data warehousing world. They’re great for encapsulating complex transformations into units that can be scheduled and respond to conditional logic via parameters. However, as teams continue building their transformation logic using the stored procedure approach, we see more data downtime, increased data warehouse costs, and incorrect / unavailable data in production. All of this leads to more stressed and unhappy developers, and consumers who have a hard time trusting their data.

If your team works heavily with stored procedures, and you ever find yourself with the following or related issues:

  • dashboards that aren’t refreshed on time
  • It feels too slow and risky to modify pipeline code based on requests from your data consumers
  • It’s hard to trace the origins of data in your production reporting

It’s worth considering if an alternative approach with dbt might help.

· 15 min read

There are many reasons you, as an analytics engineer, may want to capture the complete version history of data:

  • You’re in an industry with a very high standard for data governance
  • You need to track big OKRs over time to report back to your stakeholders
  • You want to build a window to view history with both forward and backward compatibility

These are often high-stakes situations! So accuracy in tracking changes in your data is key.

· 15 min read

Let’s set the scene. You are an analytics engineer at your company. You have several relational datasets flowing through your warehouse, and, of course, you can easily access and transform these tables through dbt. You’ve joined together the tables appropriately and have near-real time reporting on the relationships for each entity_id as it currently exists.

But, at some point, your stakeholder wants to know how each entity is changing over time. Perhaps, it is important to understand the trend of a product throughout its lifetime. You need the history of each entity_id across all of your datasets, because each related table is updated on its own timeline.

What is your first thought? Well, you’re a seasoned analytics engineer and you know the good people of dbt Labs have a solution for you. And then it hits you — the answer is snapshots!

· 13 min read

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.

· 11 min read

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.

· 5 min read

"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?

· 18 min read

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?