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At Lunar, most of our dbt models are sourcing from event-driven architecture. As an example, we have the following models for our activity_based_interest folder in our ingestion layer:

• activity_based_interest_activated.sql
• activity_based_interest_deactivated.sql
• activity_based_interest_updated.sql
• downgrade_interest_level_for_user.sql
• set_inactive_interest_rate_after_july_1st_in_bec_for_user.sql
• set_inactive_interest_rate_from_july_1st_in_bec_for_user.sql
• set_interest_levels_from_june_1st_in_bec_for_user.sql

This results in a lot of the same columns (e.g. account_id) existing in different models, across different layers. This means I end up:

1. Writing/copy-pasting the same documentation over and over again
2. Halfway through, realizing I could improve the wording to make it easier to understand, and go back and update the .yml files I already did
3. Realizing I made a syntax error in my .yml file, so I go back and fix it
4. Realizing the columns are defined differently with different wording being used in other folders in our dbt project
5. Reconsidering my choice of career and pray that a large language model will steal my job
6. Considering if there’s a better way to be generating documentation used across different models

This article covers an approach to handling time-varying ragged hierarchies in a dimensional model. These kinds of data structures are commonly found in manufacturing, where components of a product have both parents and children of arbitrary depth and those components may be replaced over the product's lifetime. The strategy described here simplifies many common types of analytical and reporting queries.

To help visualize this data, we're going to pretend we are a company that manufactures and rents out eBikes in a ride share application. When we build a bike, we keep track of the serial numbers of the components that make up the bike. Any time something breaks and needs to be replaced, we track the old parts that were removed and the new parts that were installed. We also precisely track the mileage accumulated on each of our bikes. Our primary analytical goal is to be able to report on the expected lifetime of each component, so we can prioritize improving that component and reduce costly maintenance.

I, Sung, entered the data industry by chance in Fall 2014. I was using this thing called audit command language (ACL) to automate debits equal credits for accounting analytics (yes, it’s as tedious as it sounds). I remember working my butt off in a hotel room in Des Moines, Iowa where the most interesting thing there was a Panda Express. It was late in the AM. I’m thinking about 2 am. And I took a step back and thought to myself, “Why am I working so hard for something that I just don’t care about with tools that hurt more than help?”

Hello, my dear data people.

If you haven’t read Nick & Roxi’s blog post about what’s coming in the future of the dbt Semantic Layer, I highly recommend you read through that, as it gives helpful context around what the future holds.

With that said, it has come time for us to bid adieu to our beloved dbt_metrics package. Upon the release of dbt-core v1.6 in late July, we will be deprecating support for the dbt_metrics package.

Alteryx is a visual data transformation platform with a user-friendly interface and drag-and-drop tools. Nonetheless, Alteryx may have difficulties to cope with the complexity increase within an organization’s data pipeline, and it can become a suboptimal tool when companies start dealing with large and complex data transformations. In such cases, moving to dbt can be a natural step, since dbt is designed to manage complex data transformation pipelines in a scalable, efficient, and more explicit manner. Also, this transition involved migrating from on-premises SQL Server to Snowflake cloud computing. In this article, we describe the differences between Alteryx and dbt, and how we reduced a client's 6-hour runtime in Alteryx to 9 minutes with dbt and Snowflake at Indicium Tech.

Dimensional modeling is one of many data modeling techniques that are used by data practitioners to organize and present data for analytics. Other data modeling techniques include Data Vault (DV), Third Normal Form (3NF), and One Big Table (OBT) to name a few.

Data modeling techniques on a normalization vs denormalization scale

While the relevance of dimensional modeling has been debated by data practitioners, it is still one of the most widely adopted data modeling technique for analytics.

Despite its popularity, resources on how to create dimensional models using dbt remain scarce and lack detail. This tutorial aims to solve this by providing the definitive guide to dimensional modeling with dbt.

By the end of this tutorial, you will:

• Understand dimensional modeling concepts
• Set up a mock dbt project and database
• Identify the business process to model
• Identify the fact and dimension tables
• Create the dimension tables
• Create the fact table
• Document the dimensional model relationships
• Consume the dimensional model

Teams thrive when each team member is provided with the tools that best complement and enhance their skills. You wouldn’t hand Cristiano Ronaldo a tennis racket and expect a perfect serve! At Roche, getting the right tools in the hands of our teammates was critical to our ability to grow our data team from 10 core engineers to over 100 contributors in just two years. We embraced both dbt Core and dbt Cloud at Roche (a dbt-squared solution, if you will!) to quickly scale our data platform.

Editor's note—this post assumes intermediate knowledge of Jinja and macros development in dbt. For an introduction to Jinja in dbt check out the documentation and the free self-serve course on Jinja, Macros, Packages.

Jinja brings a lot of power to dbt, allowing us to use ref(), source() , conditional code, and macros. But, while Jinja brings flexibility, it also brings complexity, and like many times with code, things can run in expected ways.

The debug() macro in dbt is a great tool to have in the toolkit for someone writing a lot of Jinja code, but it might be difficult to understand how to use it and what benefits it brings.

Let’s dive into the last time I used debug() and how it helped me solve bugs in my code.

Auditing tables is a major part of analytics engineers’ daily tasks, especially when refactoring tables that were built using SQL Stored Procedures or Alteryx Workflows. In this article, we present how the audit_helper package can (as the name suggests) help the table auditing process to make sure a refactored model provides (pretty much) the same output as the original one, based on our experience using this package to support our clients at Indicium Tech®.

At Teads, we’ve been using BigQuery (BQ) to build our analytics stack since 2017. As presented in a previous article, we have designed pipelines that use multiple roll-ups that are aggregated in data marts. Most of them revolve around time series, and therefore time-based partitioning is often the most appropriate approach.

The new dbt Certification Program has been created by dbt Labs to codify the data development best practices that enable safe, confident, and impactful use of dbt. Taking the Certification allows dbt users to get recognized for the skills they’ve honed, and stand out to organizations seeking dbt expertise.

Over the last few months, Montreal Analytics, a full-stack data consultancy servicing organizations across North America, has had over 25 dbt Analytics Engineers become certified, earning them the 2022 dbt Platinum Certification award.

In this article, two Montreal Analytics consultants, Jade and Callie, discuss their experience in taking, and passing, the dbt Certification exam to help guide others looking to study for, and pass the exam.

Testing the quality of data in your warehouse is an important aspect in any mature data pipeline. One of the biggest blockers for developing a successful data quality pipeline is aggregating test failures and successes in an informational and actionable way. However, ensuring actionability can be challenging. If ignored, test failures can clog up a pipeline and create unactionable noise, rendering your testing infrastructure ineffective.

Imagine you were responsible for monitoring the safety of a subway system. Where would you begin? Most likely, you'd start by thinking about the key risks like collision or derailment, contemplate what causal factors like scheduling software and track conditions might contribute to bad outcomes, and institute processes and metrics to detect if those situations arose. What you wouldn't do is blindly apply irrelevant industry standards like testing for problems with the landing gear (great for planes, irrelevant for trains) or obsessively worry about low probability events like accidental teleportation before you'd locked down the fundamentals. 

When thinking about real-world scenarios, we're naturally inclined to think about key risks and mechanistic causes. However, in the more abstract world of data, many of our data tests often gravitate towards one of two extremes: applying rote out-of-the-box tests (nulls, PK-FK relationships, etc.) from the world of traditional database management or playing with exciting new toys that promise to catch our wildest errors with anomaly detection and artificial intelligence. 

Between these two extremes lies a gap where human intelligence goes. Analytics engineers can create more effective tests by embedding their understanding of how the data was created, and especially how this data can go awry (a topic I've written about previously). While such expressive tests will be unique to our domain, modest tweaks to our mindset can help us implement them with our standard tools. This post demonstrates how the simple act of conducting tests by group can expand the universe of possible tests, boost the sensitivity of the existing suite, and help keep our data "on track". This feature is now available in dbt-utils. 

In seventh grade, I decided it was time to pick a realistic career to work toward, and since I had an accountant in my life who I really looked up to, that is what I chose. Around ten years later, I finished my accounting degree with a minor in business information systems (a fancy way of saying I coded in C# for four or five classes). I passed my CPA exams quickly and became a CPA as soon as I hit the two-year experience requirement. I spent my first few years at a small firm completing tax returns but I didn't feel like I was learning enough, so I went to a larger firm right before the pandemic started. The factors that brought me to the point of changing industries are numerous, but I’ll try to keep it concise: the tax industry relies on underpaying its workers to maintain margins and prevent itself from being top-heavy, my future work as a manager was unappealing to me, and my work was headed in a direction I wasn’t excited about.

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.

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.

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.

Let’s discuss how to convert events from an event-driven microservice architecture into relational tables in a warehouse like Snowflake. Here are a few things we’ll address:

• Why you may want to use an architecture like this
• How to structure your event messages
• How to use dbt macros to make it easy to ingest new event streams

For years working in data and analytics engineering roles, I treasured the daily camaraderie sharing a small office space with talented folks using a range of tools - from analysts using SQL and Excel to data scientists working in Python. I always sensed that there was so much we could work on in collaboration with each other - but siloed data and tooling made this much more difficult. The diversity of our tools and languages made the potential for collaboration all the more interesting, since we could have folks with different areas of expertise each bringing their unique spin to the project. But logistically, it just couldn’t be done in a scalable way.

So I couldn’t be more excited about dbt’s polyglot capabilities arriving in dbt Core 1.3. This release brings Python dataframe libraries that are crucial to data scientists and enables general-purpose Python but still uses a shared database for reading and writing data sets. Analytics engineers and data scientists are stronger together, and I can’t wait to work side-by-side in the same repo with all my data scientist friends.

Going polyglot is a major next step in the journey of dbt Core. While it expands possibilities, we also recognize the potential for confusion. When combined in an intentional manner, SQL, dataframes, and Python are also stronger together. Polyglot dbt allows informed practitioners to choose the language that best fits your use case.

In this post, we’ll give you your hands-on experience and seed your imagination with potential applications. We’ll walk you through a demo that showcases string parsing - one simple way that Python can be folded into a dbt project.

We’ll also give you the intellectual resources to compare/contrast:

• different dataframe implementations within different data platforms
• dataframes vs. SQL

Finally, we’ll share “gotchas” and best practices we’ve learned so far and invite you to participate in discovering the answers to outstanding questions we are still curious about ourselves.

Based on our early experiences, we recommend that you:

✅ Do: Use Python when it is better suited for the job – model training, using predictive models, matrix operations, exploratory data analysis (EDA), Python packages that can assist with complex transformations, and select other cases where Python is a more natural fit for the problem you are trying to solve.

❌ Don’t: Use Python where the solution in SQL is just as direct. Although a pure Python dbt project is possible, we’d expect the most impactful projects to be a mixture of SQL and Python.

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?