What does it mean to "be technical"? What makes a great analytics engineer? How can individuals "develop technically", how can managers "foster technical growth", and how can companies "hire technical people"? It's crucial to understand the component skills that build into great analytics engineering outcomes.

As it turns out, it's not so different from how fashion designers go from prompt to runway look. Join Ashley Sherwood (HubSpot) as she breaks down the parallels between fashion design and analytics engineering work and how small daily design decisions can compound to a massive impact on data teams' abilities to grow their skills and serve stakeholders.

Check the slides here: https://docs.google.com/presentation/d/1HDzAzHhWy4q_cXASB1F3EfTWivWlijCRu5e-CIbaXqI/edit?usp=sharing

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

As analytics engineers, we make impact by building analytics things (models, pipelines, visualizations) that help stakeholders make decisions about what to do next. What if we could also make impact by driving a culture of experimentation—which will help those same stakeholders make decisions too?

Join Adam Stone (Netlify) as he draws on his vast experimentation experience and explains how analytics engineer can use a combination of a program-building mindset, organizational mentoring (and cheerleading), and off-the-shelf tools to partner with product and engineering teams to quickly spin up meaningful experimentation.

Check the slides here: https://docs.google.com/presentation/d/1vWfhfTnC9-NV-qrQLTkGk4qgdi-19JA8E3p6fpniQe0/edit?usp=sharing

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

What do you do when you find out that your team is being tasked with building a single platform that should be able to serve everyone's data needs, no matter whether they are internal (from within your company) or external (your customers)? What's more it's expected to be fast, stable, granular, sophisticated, simple, scalable, usable, easy to maintain, compatible… the list goes on.

Well, time to find a new-school solution. We'll walk you through our story of how and why we built Slido's dataAPI using everyone's favourite Analytics Engineering tool, dbt.

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

Most analysts don’t become analysts to build dashboards. We don’t become analysts to do data pulls, or clean up messy data, or put together pitch decks. We become analysts to do impactful, strategic analysis. This is our calling; it’s the most valuable work that we do; and it’s why we put up with the rest of our job—for that afternoon with nothing but a big question, a clear calendar, and a trajectory-changing aha moment buried somewhere in our well-prepped datasets.

But the rapid rise of analytics engineering should make us question all of this. Is strategic analysis actually the holy grail of analytics? Is it the most valuable thing we could do? Is it even what we want to do?

In chasing this ambition, Benn Stancil (Mode) thinks we’ve lost sight of something even more important—and potentially, more interesting: Designing operational models. These frameworks, which are a natural extension of the semantic models built by analytics engineers, are often more valuable than any dashboard, any dataset, or any deep dive analysis.

In his talk, Benn will share what these models are, why they’re valuable, and why, in our eternal quest to both quantify our value and to find work we love, they could prove to be our holy grail we’ve always been looking for.

Check the slides here: https://docs.google.com/presentation/d/1lOH6Sb8DQnnlmZkYOlqqHgQeXKkUEQCm_LOxsjBRJlM/edit?usp=sharing

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

There’s a good chance you’re an analytics engineer who just sort of landed in an analytics engineering career. Or made a murky transition from data science/data engineering/software engineering to full-time analytics person. When did you realize you fell into the wild world of analytics engineering?

In this session, Michael Chow (RStudio) draws upon his experience building open source data science tools and working with the data science community to discuss the early signs of a budding analytics engineer, and the small steps these folks can take to keep the best parts of Python and R, all while moving towards engineering best practices.

Check the slides here: https://docs.google.com/presentation/d/1H2fVa-I4D8ibanlqLutIrwPOVypIlXVzEITDUNzzPpU/edit?usp=sharing

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

The year is 2022, data teams are earning their stripes, data work is being valued, and our organizations recognize that analytics engineering is no longer the future of data teams—it’s the present. Join Mong Dang as she discusses how Aritzia, a normal company with a talented and growing roster of data team members, scaled up their delivery of quality data through analytics engineering and leveraged their data product management approach. Like all experimental work, Mong will cover what they tried, what has been working, what didn’t work, and where they still have questions and can learn more on.

Check the slides here: https://drive.google.com/file/d/1iZkrLvF6UkzE_XfF06B4dZncRSMo6nNY/view?usp=sharing

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

Roleplaying games? Roleplaying games in data? You read that right. To Ian, there's more commonality in roleplaying games and the data world than most of us think. In this session, Ian Fahey (dbt Labs) will draw on his vast experiences in roleplaying games and analytics engineering work to walk through the adventuring classes of "the world's most popular tabletop roleplaying game" (Dungeons and Dragons) and talk about how they can inform data professionalism.

Check the slides here:https://docs.google.com/presentation/d/16Wm4ChDPORvEkDxUu3-mHBYRLJtwUTRdP7rj-LIrUB4/edit#slide=id.g1571952a68b_0_12

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

As analysts lean further into analytics engineering workflows, they’ll need to source opportunities for more hands-on experience. Forunately, this is achieveable just by applying a fresh framework to existing analytics projects. In this session with Brittany Krauth (Degreed), you’ll learn how to approach common problems through the analytics engineering workflow.

Check the slides here: https://docs.google.com/presentation/d/1RO7EAbHqcxsRWp40EKusANwD5IwUAoEs/edit?usp=sharing&ouid=110788023771657617483&rtpof=true&sd=true

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

Academia/open science is an as-yet untapped market for analytics engineering, as well as one that could majorly benefit from the tight coupling of data transformation and software engineering best practices. But introducing dbt into this context comes with its own set of challenges. In this session, Šimon Podhajský (iLife Technologies), explains what’s slowing progress here,, and what academics can do to progress this work.

Check the slides here: https://docs.google.com/presentation/d/1aw_cs6V0n-oT9Lp7Vq3MNcRbthEFJEYwcvkCBfuzlR0/edit?usp=sharing

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

See how visual data modeling and dbt combine to improve interaction and understanding between analytics engineering practitioners, product owners, and business partners. We will demonstrate conceptual and logical modeling techniques and diagrams to establish common understanding, enhance business partner collaboration, enhance translation of requirements, and ultimately complement analytics engineering within dbt to improve time to value. Demonstrate how to pair data modeling concepts (conceptual, logical, physical) and tools (SqlDBM) to engage your customers and inform the analytics engineering with dbt and Snowflake. We will show how this workbench and tools complement the analytics lifecycle for engineers and data consumers alike. The workbench includes a dbt, a visual modeling tool, and phData Toolkit CLI.

This session requires pre-registration. Sign up here. If session is filled you are welcome to come to the room and join the waitlist onsite. Open seats will be made available 10 minutes after session start.

Check the slides here: https://docs.google.com/presentation/d/1fJhaMGvD7TvVft4nEJYhMRhyanQTw3lbzLrgZFsmj-0/edit?usp=sharing

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

Data apps are the next wave in analytics engineering. The explosion of data volume and variety combined with an increasing demand for analytics by consumers, and a leap in cloud data technologies triggered an evolution of traditional analytics into the realms of modern data apps. Question is: How do you prepare for this wave? In this session we’ll explore real-world examples of modern data apps, and how the modern data stack is advancing to support sub-second and high concurrency analytics to meet the new wave of demand. We will cover: performance challenges, semi-structured data, data freshness, data modeling and toolsets.

Check the slides here: https://docs.google.com/presentation/d/1MC18SgT_ZHOJePjYizz_WT7dVveaycNw/edit?usp=sharing&ouid=110293204340061069659&rtpof=true&sd=true

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

As analytics engineers and data people, we know the value we create in our own blood, sweat, and dbt models. But how is this value actually realized in practice? In this talk, David Jayatillake (Metaplane) draws on his experiences to discuss the processes, ways of thinking, tooling, and governance needed to realize the benefits from analytics engineering work in the greater organization.

Check the slides here: https://docs.google.com/presentation/d/1VmmqNQsrv1t0uuV81O6PJQ1XASyLRGxvAdB8eWIG9TQ/edit?usp=sharing

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.