“Excellent content, delivered in a convenient way and enthusiasm oozing from every pore - couldn’t hope to take it all in at the time but will spend holidays catching up on demand!” - Coalesce 2021 attendee feedback
More than 8,000 data humans joined us for Coalesce 2021. While we’re astounded and humbled by that number, we know even more will be tuning in over the next few weeks, during the “Coalesce Catchup” period that tends to run through the holiday season. In 2020 we had over 4,000 live attendees, but over 7,000 views of the Coalesce 2020 playlist on the dbt YouTube channel in the months following.
That tells me two things:
- You’re busy! Attending live is a big commitment (which is why we post videos, transcripts, slides, and the corresponding Slack chat for every session).
- Coalesce presentations have real value. Folks return to the same talks many times as a source of reference, inspiration, and maybe even validation?
With Coalesce 2021 wrapping around the same time as your end-of-year rush, you might be in the pool of people planning to hit play on more than 67 hours of Coalesce content… 😅
OR, if you were also planning to sleep and eat at some point… you might consider one of two condensed tracks:
- For the team preparing to scale (7.5 hrs): If you feel comfortable with core concepts, and are more interested in exploring new frameworks and tools, or are considering ways to refactor your current team structure to prepare for rapid growth, jump straight to that track instead!
- For the emerging analytics engineer (7 hrs): This blog is written for those just starting their analytics engineering journey, or those on a small team still working to establish best practices with limited budget and tooling. If that’s you, you’re in the right place.
For the Emerging Analytics Engineer
Module 1: Understanding analytics engineering (2 hrs)
Still have questions about what analytics engineering actually is, or how to incorporate it into your existing workflow? You’ve come to the right module. Learn how we got here, where we think we’re going, and exactly what analytics engineering looks like for both the humans practicing and the organizations they represent. This module is especially useful for members of the data team looking for a crash course in what it might take to redefine their roles.
- Analytics engineering everywhere (27 mins)
- Upskilling from insights analyst to an analytics engineer (29 mins)
- Data as engineering (24 mins)
- Git for the rest of us (40 mins)
Looking for additional resources? Check out these articles:
- The Analytics Engineer, by Michael Kaminsky
- What is analytics engineering, by Claire Carroll
- How to find a role in analytics engineering, by Danielle Leong
Module 2: Building foundational frameworks (1 hr 20 mins)
If you’re ready to bring analytics engineering to your team (even if you’re a team of one!) this module will show you have to develop baseline frameworks, best practices, and migration plans that you can use to ease the transition and ensure resilience at scale. You’ll also see how other growth-stage teams made the leap with very modest headcount and budgets.
- Implementing and scaling dbt Core without engineering resources (30 mins)
- No silver bullets: Building the analytics flywheel (23 mins)
- From 100 spreadsheets to 100 data analysts: the story of dbt at Slido (28 mins)
- How to build a mature dbt project from scratch (30 mins)
Looking for additional resources? Check out these articles:
- The Analytics Engineering Guide, many contributors, curated by dbt Labs
- Scaling data analytics with software engineering best practices, by Nubank
Module 3: Determining data team structure (1 hr 54 mins)
You may already have an ideal data team in place today, and need only recalibrate roles and responsibilities. Or you may be looking to draft a hiring plan that will see your headcount triple in the next year. This module includes sessions that share how to think about building new data teams and improving data culture.
- Don’t hire a data engineer… yet (40 mins)
- Scaling Knowledge: Why dbt Labs is making the bet on a data literate organization (27 mins)
- Analytics engineering for storytellers (20 mins)
- To all the data managers we’ve loved before (27 mins)
Looking for additional resources? Check out these articles:
- Spinning up an analytics engineering team, by Monzo
- Building a data team, curated by dbt Labs
- How to choose the right structure for your data team, by Barr Moses
Module 4: Choosing your tooling (1 hr 43 mins)
You’re ready to get moving, but there’s one more set of decisions you’ll need to make. Which tooling will support the structure you’ve established for your team? Or alternatively, which tooling will change how you’ve thought about structuring your team? This module includes talks that shed some light on several layers of the modern data stack.
- dbt 101 (25 mins)
- Building an open source data stack with Lightdash (25 mins)
- Firebolt Deep Dive - Next generation performance with dbt (23 mins)
- The Modern Data Stack: How Fivetran Operationalizes Data Transformations (30 mins)
Looking for additional resources? Check out these articles:
- The modern data stack: Past, present, and future, by Tristan Handy
- The beginner’s guide to the modern data stack, by Prukulpa
What now?
Coalesce is full of portable frameworks and practical advice. But there’s something to be said for talks that just remind us why we started down this path to begin with. The below sessions reinvigorated our entire team, and reminded us why this moment feels so unique, and important. I hope they have the same impact on you.
- The Metric System: The dbt Labs product keynote by co-founder Drew Banin generated more lasting conversation across social media than any other session. It also garnered 888 comments in the dbt community Slack.
- How Big is this Wave?: Martin Casado (A16z) joined Tristan (dbt Labs CEO and co-founder) in the session that drew the most live attendees to chat about what makes this wave of data tech different from any that came before.
- The Modern Data Experience: Famed Substack author (and founder of Mode Analytics) focused on the gaps between solutions in the modern data stack, and how the way we fill them will make or break the future of this market.
- The Future of Data Analytics: This power panel of VCs discuss the latest data trends from data quality to data ops, and why the seemingly absurd valuations we’re seeing lately might not be entirely unfounded.
Last modified on: Apr 24, 2022
