dbt has changed the game for data practitioners, bringing velocity, organizational efficiency, and increased trust to modern analytics workflows. But what happens to your dbt models after they’ve been built? Too often the value you create goes untapped by the business, or accessed only by a select few. dbt Labs and ThoughtSpot are teaming up to unleash the true potential of your transformed data. Learn how to deliver trustworthy data and insights to frontline users at scale with safe, reliable self-service analytics.

Check the slides here: https://docs.google.com/presentation/d/1XFyP8d0wkfnA1_59zb1zbNYInSSzdI2qe09f6OkhRE0/edit?usp=sharing

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

When does your team decided it’s time to move beyond a singular dbt run? For most analytics engineers, there comes a time when the dbt run commands on fixed schedules simply won’t make the cut. Join Prratek Ramchandani (Vox Media) as he breaks down an alternative approach to orchestrating your dbt project with Dagster that balances meeting SLAs with safely handling the edge cases a simple schedule-based dbt run might create.

Check the slides here: https://docs.google.com/presentation/d/1zivYO_EpN6T9JYM9HjAJAz3bK3e2TREwdKffylkzuUw/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/.

Analytics at a startup….something that might scare most data folks. But not Lindsay Murphy of Maple! Join Lindsay as she draws on her experiences to demystify and template the process behind using dbt to create robust self-service analytics practices at startups and other small companies.

Check the slides here: https://docs.google.com/presentation/d/1GMe4K3UQgJESozHV2-p19OwJoBQJ3VEfZaX-cthsYdU/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/.

Using data to grow revenue is a lofty goal thrown around by most business leaders these days, but leaders (and the data teams that support their goals) often don’t have a clear understanding of what needs to happen on the ground to actually make this come to reality. Data teams are looking for the right combination of modern tools, best practices, and company culture to meaningfully impact their company’s goals and revenue.

In this panel discussion, Census CEO Boris Jabes, Carta VP of Data and Analytics Julia King, and ClickUp Head of Analytics Marc Stone will break down what it really means to operationalize your data to drive revenue, and what every company can do to make this a reality, no matter their team size.

Check the slides here: https://docs.google.com/presentation/d/1CDuaVUm3yxwbcjNXMv1JcAv9PdfZFM80kwbvl9yYRtU/edit?usp=sharing

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

Product manager turned analytics manager, Caroline Rhodes (Wellframe), asks whether analytics teams really need PMs, or whether their presence can actually slow down, or misdirect your team. Attendees of this session will walk away knowing how to make this call based on their own use cases and ambitions.

Check the slides here: https://docs.google.com/presentation/d/1MAW_WfTGdygR7pBj7utwV5o--vlKQbLM25XhmLH8aEU/edit?usp=sharing

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

In this session participants will learn:

1. How a product analytics platform can help you understand User Flows — how users are flowing in and out of certain features.

2. The impacts that certain features can have on engagement and retention. - Understanding User and Product-Feature Usage Patterns - Quantify Impact on User Engagement - Quantify Impact on User Retention

3. Prioritize Roadmap Features Using Data Insights

4. Ensure insight fidelity and compliance with data quality and privacy tools that a PM can use

5. Increase product adoption by activating user behavior data

Check the slides here: https://docs.google.com/presentation/d/1G6qIbWkzMp_IXACvuJw1oBb46BjbV7_jCeWk5P6rOlk/edit?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/.

Supplying users with the most relevant data is a time-consuming challenge. By combining the power of dbt with machine learning (ML) analysis, companies are able to focus on the most relevant segments in the data that have the greatest impact on key metrics and how they change over time. Leveraging the Sisu Decision Intelligence Engine, dbt users, analysts, and data scientists focus their efforts where they have the greatest impact. Sisu's AI/ML-powered automated analytics identifies relevant data and predicts changes, helping focus data exploration, speed insights, and drive successful outcomes.

Check Notion document here: https://www.notion.so/6382db82046f41599e9ec39afb035bdb

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

Many transformations are fine candidates for concretizing with dbt. But there are transformations that live in the data science world that are not well-suited for dbt—and probably for good reason. Consider the total set of all transformations, from mandatory pre-processing steps to sophisticated statistical transformations (e.g., converting data types versus computing robust measures of central tendency). The question quickly becomes: How do data teams decide which transformations to push down to dbt and which to leave up in the notebook?

In this panel discussion led by Allan Campopiano (Deepnote), analytics engineers, data engineers, and data scientists discuss what transformation means to them, where and when transformation happens in their stack, and how to collaborate effectively between high- and low-level forms of transformation.

Check the slides here: https://docs.google.com/presentation/d/1uqi1C2gpBnsMp-BTvjltmjlnedWKqEzwonZjrewOWNk/edit?usp=sharing

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/.

As data science and machine learning adoption grew over the last few years, Python moved up the ranks catching up to SQL in popularity in the world of data processing. SQL and Python are both powerful on their own, but their value in modern analytics is highest when they work together. This was a key motivator for us at Snowflake to build Snowpark for Python: to help modern analytics, data engineering, and data science teams generate insights without complex infrastructure management for separate languages.

Join this session to learn more about how dbt's new support for Python-based models and Snowpark for Python can help polyglot data teams get more value from their data through secure, efficient and performant metrics stores, feature stores, or data factories in the Data Cloud.

Check Notion document here: https://www.notion.so/6382db82046f41599e9ec39afb035bdb

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

Pulling data directly out of application databases is commonplace in the MDS, but also risky. Apps change quickly, and application teams might update database schemas in unexpected ways, leading to pipeline failures, data quality issues, data delivery slow-downs. There is a better way. In his session, Charlie Summers (Merit) describes how their organization transforms application event streams into analytics-ready tables, more resilient to event scheme changes.

Check the slides here:https://docs.google.com/presentation/d/1K5PcoVshiHKZs_xI3K4P5JRNYTkbmnQJPMl8NmBlGfo/edit

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/.

Today’s analytics engineers weren’t at the top of their class for any one thing in particular—they’re misfits—having tried and maybe even failed at any number of things. But these misadventures always revealed a common trait—insatiable curiosity for the way things work—the logic and order applied to anything from the music industry to particle physics. In this talk, Andie DeLeon (Thirty Madison) shares how current and aspiring analytics engineers can translate their experience in “passion projects” to driving real business impact for organizations in any domain.

Check the slides here: https://docs.google.com/presentation/d/14-pTSULJCn6j7PKJB81U33nClurvds25Hr1eZkvsfI8/edit?usp=sharing

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

When the content analytics team at Spotify adopted dbt and shifted away from an internally developed transformation tool, they needed to figure out how to access data produced by other teams using sharded partitions. Enter: Waluigi. Nick Baker, Brian Pei, and Mitchell Silverman show us how an internal package used to safely and smoothly ingest the data they need also helped empower other data teams to more easily adopt dbt and leverage the data they produce.

Check the slides here: https://docs.google.com/presentation/d/1uAzfKa2Usbbr7J6jcI-IwdK1gPTKEsWSgZEfTU5bulM/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/.

In between JVM languages, high-maintenance frameworks and academic papers, streaming remains a hard beast to tame for most of us. What if nothing had to change, and streaming just meant…still writing dbt models? At Materialize, we’re exploring how to make the most of dbt for streaming — from real-time analytics to continuous testing, and beyond! Join us to learn how to get started with no blood, sweat or tears, using the Jaffle Shop as a playground. Our toolbox? A database that feels like Postgres but works like all the streaming systems you’ve been avoiding, some SQL and a dash of magic.

Check the slides here: https://docs.google.com/presentation/d/11PANQElVxtzqgzmRCcQfZy24vdMeYDokpxr7LdlrbrE/edit#slide=id.g105b4fffa32_0_942

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

The "socio" is inseparable from the "technical". In fact, technological change often begets social and organizational change.

And in the data space, the technical changes that some now refer to as the "modern data stack" call for changes in how teams work with data, and in turn how data specialists work within those teams. Enter the Modern Data Team.

In this talk, Abhi Sivasailam will unpack the changing landscape of data roles and teams and what this looks like in action at Flexport. Come learn how Flexport approaches data contracts, management, and governance, and the central role that Analytics Engineers and Product Analysts play in these processes.

Check the slides here: https://docs.google.com/presentation/d/1Sgm3J6EkeKQf5D1MKopsLLAMOhAZ05CxDlei2mbDE90/edit#slide=id.g16424dcc8d3_0_1145

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

With Trino (formerly PrestoSQL) and dbt combined, you can get faster access to your data and the ability to analyze data across multiple data sources with ease. Extract, load and transform data in your data lakehouse easier than ever before using dbt’s Trino adapter. Join Brian Zhan and Tom Nats as they talk about the new dbt connector for Trino and how it works, along with a demo showing how easy it is to deploy, build and serve up analytics using dbt and Starburst Galaxy.

Check the slides here: https://docs.google.com/presentation/d/1-A-mfc1RIj87ypz6KeZvxK62QLaGthmMqBPy10vNnDk/edit?usp=sharing

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

Analytics work mirrors product development: identify a user need, build a minimum viable product to address that need, evaluate the impact and iterate. In this talk, Michal Kolacek, analytics engineer at Slido describes how MVP-like thinking can help data teams counterbalance and complement the standardized approaches of dbt.

We will walk through Slido’s evolution in their approach, tooling and the vision of building better data products using Deepnote notebooks. Finally, we will take a look under the hood of the new dbt integration in Deepnote and outline how data teams can use it to accelerate model prototyping and metrics workflows.

Check the slides here: https://docs.google.com/presentation/d/1-L7ndud6z5gsFtF3WdjA6AVG40_vrCAcVRNarqWNtPg/edit?usp=sharing

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/.

How do you train and enable 20 data analysts to use dbt Core in a short amount of time?

At M1, engineering and analytics are far apart on the org chart, but work hand-in-hand every day. M1 engineering has a culture that celebrates open source, where every data engineer is trained and empowered to work all the way down the infrastructure stack, using tools like Terraform and Kubernetes. The analytics team is comprised of strong SQL writers who use Tableau to create visualizations used company wide. When M1 knew they needed a tool like dbt for change management and data documentation generation, they had to figure out how to bridge the gap between engineering and analytics to enable analysts to contribute with minimal engineering intervention. Join Kelly Wachtel, a senior data engineer at M1, explain how they trained about 20 analysts to use git and dbt Core over the past year, and strengthened their collaboration between their data engineering and analytics teams.

Check the slides here: https://docs.google.com/presentation/d/1CWI97EMyLIz6tptLPKt4VuMjJzV_X3oO/edit?usp=sharing&ouid=110293204340061069659&rtpof=true&sd=true

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