Scaling data teams from zero is hard: there are no easy shortcuts, and it is hard to find clear examples to learn from. That’s why we are very excited to co-present the work we did at On Deck over the last year: starting in Summer 2021 we built a data team from scratch, relying heavily on dbt as the core data modelling environment. Come hear us talk about how we set up the team, prioritised the many different requirements from an ever expanding team of stakeholders and, after just a few months, succeeding in moving On Deck away from a no-code data architecture (with more than 400 SaaS tools) and towards a centralised data model in dbt. We think the lessons (and especially the pitfalls) are worth telling!

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

How do you efficiently scale your analytics stack when your data and data team grows 10x in 2 years? How do you even start prioritizing what gets done when there's that much growth? In this talk, Felipe Leite and Stephen Pastan of Miro unpack their shift to a Modern Data Stack and share the vital technical changes they made to build a scalable and tech-forward data stack. Come join them in learning how they got to where they are today and what they’re working on for the future. More details coming soon!

Check the slides here: https://docs.google.com/presentation/d/1lLoRBYAv8wlJQuhSrflX4F6y4lqV3SnDNAU7BYxHYR8/edit#slide=id.p

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

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/.

Since the dawn of time (or at least the last few years), the proserv team has been “dbt_project_evaluator”s. They've written articles, given talks, created training courses, and personally delivered just a truly obscene amount of audits. Up until now, evaluating your own dbt project—even with every aforementioned resource—would be incredibly time consuming. To quote dbt Lab’s SQL style guide, “brain time is expensive.” Enter: dbt_project_evaluator. In this talk, Grace Goheen (dbt Labs) which share how this package enables analytics engineers to follow dbt Labs own best practices by automatically curating a list of improvements, in the dbt-language of “models” and “tests” that they already know and love. By decreasing the “discovery” period, analytics engineers can use their brain time to work on actually implementing the recommended changes.

Check the slides here: https://docs.google.com/presentation/d/1U7CaoSceXumbzlPGqqAQukaz1YdOgT46sb-M6x_LNCw/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/.

I have spoken with many data people about their most hated tool: the “service desk” used to manage the requests of others.

One team has a Slack channel called “data-sucks.” The name tells you much about people’s feelings. Others use Google forms, JIRA Service Desk, or some horrifying combination.

Teams debate the ways that they can prevent, or reduce, these requests in the first place. Should we make requests more difficult? Are we stuck in the service trap? Should we enable self-service analytics?

Initiatives are undertaken, and afterwards is a terrible truth: the questions and our loathing remain. And the others? They still hate making the requests, too.

Why do we all feel this way, and why are we still unable to do anything about it?

Check the slides here: https://docs.google.com/presentation/d/1-JmXX1RZHLf3VKRZJoPHw-QFUodODzOmu13GJSVdkM4/edit?usp=sharing

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

A Senior Business Analyst, an Analytics Engineer, and a Data Quality Engineer walk into the office. All three of them ask for dbt. Why? Join Coco Haury (WEX, Inc.) to hear about the universal appeal of dbt to folks outside traditional data engineer and analyst roles, and how dbt has helped WEX modernize their entire enterprise data platform.

Check the slides here: https://docs.google.com/presentation/d/1XNphR_il_3SGy9dU7G7PxmVoCXCCWBuFMvZYESN6KgE/edit?usp=sharing

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

Data teams aren't only working in Excel and Tableau anymore. We're working in GitHub, VSCode, BigQuery, and our command line. We have the modern data pipeline and we're developing a lot more like engineers...but our development workflows are anything but modern. Why don't we get developer previews? Or the ability to test our changes to anything downstream of dbt? Why do our tools not talk to each other? We believe that analytics engineers deserve better and we want to show you what "better data development" could look like in the modern data pipeline (we promise, it's really nice).

Check the slides here: https://docs.google.com/presentation/d/1NtAOknFDmJiIQD6cSTbhmfZ-6ZFhKKH3GKcEZ3AL5eg/edit?usp=sharing

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

In this talk, Marielle Dado (Paddle) will introduce the concept of knowledge construction (a theory that featured heavily in their previous life as a learning psychologist/cognitive scientist), why they think analytics engineers should see themselves as "knowledge creators," and how this framing can lead to less ad-hoc querying and more robust data models.

Check the slides here: https://docs.google.com/presentation/d/1aMwNiFd7B5pArOP8Nc_jfHCLtNvKimaUJFUuMMPQC04/edit?usp=sharing

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

Growing demand for data puts Analytics Engineers under pressure to move quickly, without introducing bugs to their models. If you find yourself pushing dbt’s testing framework to the limit, or waiting for careful code reviews before deploying to production, this talk is for you. Join Egor Gryaznov, CTO of Bigeye and former Staff Data Engineer at Uber, to learn how to take your workflow to the next level with blue-green deployments. This talk will cover: -CI/CD pipeline best practices for Analytics Engineers -Automatically validating pipeline changes with blue-green deployments -Tracking data changes through merge requests for visibility and reliability More details coming soon!

Check the slides here: https://docs.google.com/presentation/d/1zkzKkhjm1WshCMSOksfi24BEGK62wrLOGKKEg0I0Yhg/edit#slide=id.g11c331d5e28_0_6192

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

Everyone is talking about data lineage these days, and for a good reason. Data lineage helps ensure better data quality across your modern data stack. But not everyone speaks the same lineage language. Data engineers use lineage for impact and root cause analysis. Analysts and Analytics engineers use lineage to trace jobs and transformations in their warehouses. And consumers use lineage to understand why data never reached their expected destination. This results in a narrow, siloed view lineage in which only one group benefits. It’s time to stop using siloed lineage views for pretty graphs and start using end-to-end lineage to drive focused actions. In the talk, you will learn:

• How data quality tailors to specific needs of data engineers, analysts, & consumers

• How data lineage should drive actions

• A real-world example of end-to-end data lineage with Airflow, dbt, Spark, and Redshift

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 the slides here: https://docs.google.com/presentation/d/1xJEyfg81azw2hVilhGZ5BptnAQo8q1L7aDLGrnSYoUM/edit?usp=sharing

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

Do you enjoy waking up in the morning and playing the daily Wordle puzzle? Have you been wishing there was a similar game for you to play, but was built specifically for data folks? Well, you are in luck!

Join Joe Markiewicz (analytics engineer by day, video game maker by night) as he explains his inspiration and how he leveraged dbt and BigQuery to create a new video game aimed at helping experienced analysts keep their SQL skills sharp and data newcomers increase their SQL literacy.

Check the slides here: https://docs.google.com/presentation/d/1C1qUZEcpfBa6oA_CTHGx3GR1WLW3s2adVXnX2BRKVWA/edit

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/.

dbt’s powerful capabilities allow data teams to deliver data products and analytics solutions to solve business problems faster than ever. Yet still, even with the best modern technologies, challenges arise. How can you be certain what your building will stand up to changing requirements? How can you connect disparate parts of your business to derive new insights? The answer may be a blast from the past—but the fundamentals never change. Learn how to apply fundamental techniques—like dimensional modeling—to modern tools, helping you to build scalable and reusable solutions to solve data problems today, and in the future.

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

for a few use cases like static and passive data catalogs. However, active metadata can be the key to unlock a variety of use cases, acting as the glue that binds together our diverse modern data stacks (e.g. dbt, Snowflake, Fivetran, Databricks, Looker, and Tableau) and diverse teams (e.g. analytics engineers, data analysts, data engineers, and business users)! At Atlan, we’ve worked closely with modern data teams like WeWork, Plaid, PayU, SnapCommerce, and Bestow. In this session, we’ll lay out all our learnings about how real-life data teams are using metadata to drive powerful use cases like column-level lineage, programmatic governance, root cause analysis, proactive upstream alerts, dynamic pipeline optimization, cost optimization, data deprecation, automated quality control, metrics management, and more. P.S. We’ll also reveal how active metadata and the dbt Semantic Layer can work together to transform the way your team works with metrics!

Check the slides here: https://docs.google.com/presentation/d/1xrC9yhHOQ00qWt-gVlgbakRELg2FzEPt-RwMsUWzdZA/edit?usp=sharing

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

Data teams are tasked with integrating a growing number of data sources, and enabling broad, self-service access to a consistent and unified view of that data to a growing number of technical and non-technical data consumers for analytics. In this session, learn how dbt and the Dremio open lakehouse platform work together to simplify data architectures, unify data sources, and get insights into the hands of data consumers fast, and how the new connector delivers a seamless user experience across platforms.

Check the slides here: https://docs.google.com/presentation/d/1ovzCrr1DnPF0n0JMVnPrceAcOZHSyD_aCaayjK8oISo/edit?usp=sharing

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

The Databricks Lakehouse Platform unifies the best of data warehouses and data lakes in one simple platform to handle all your data, analytics and AI use cases. Databricks now includes complete support for dbt Core and dbt Cloud and you will hear from Conde Nast using dbt and Databricks together to democratize insights. We will also share best practices for developing and productionizing dbt projects containing SQL and Python, governing data with standard SQL, and exciting features on our roadmap such as materialized views for Databricks SQL.

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

When Phoenix Jay (K Health) started her Master's in Applied Analytics, it was with the assumption that she’d quickly land a gig in data science. But by the time she graduated, the field had changed—as had every Data Scientist job posting. This shift brought uncertainty, but also opportunity. Learn how she adjusted her path to begin applying “classical” data science paradigms to analytics.

Check the slides here: https://docs.google.com/presentation/d/19DviXCiciRh3dq-5cSTXkPqA7CppUUKAsfpm6dmn0l8/edit?usp=sharing

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

"They’re looking for someone technical, but business-oriented.” Says the newly minted analytics engineer—quietly content in her ability to be everything to everyone. But "purple" work can still be perilous: roadmap uncertainty, stakeholder confusion, an unending list of new things to learn... In practice, becoming an efficient generalist requires a specific skill set, one not often taught in schools. In this talk, Stephen Bailey (Whatnot) shares how to navigate the most complex, but rewarding bits of interdisciplinary data work.

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

Lineage is a critical component of any root cause, impact analysis, and overall analytics heath assessment workflow. But it hasn’t always been easy to create, particularly at the field level. In this session, Mei Tao, Helena Munoz, and Xuanzi Han (Monte Carlo) tackle this challenge head-on by leveraging some of the most popular tools in the modern data stack, including dbt, Airflow, Snowflake, and ANother Tool for Language Recognition (ANTLR). Learn how they designed the data model, query parser, and larger database design for field-level lineage—highlighting learnings, wrong turns, and best practices developed along the way.

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

Insensatez in Portuguese means folly, unwisdom. In other words, doing something you deep down know you shouldn't. Data practitioners don't always have the right incentives or organizational influence to advocate for the solutions that they believe are best fit for the problems they are solving. Join Felippe Felisola Caso (Loft) as he bravely wades into the awkward and often unspoken feelings between leaders and analysts. Through an exploration of organizational feelings and perspectives, attendees can expect to walk away with a greater capacity for empathy, as well as proven strategies for improving communication and building alignment.

Check the slides here: https://docs.google.com/presentation/d/1TKtveMXtFBoCALp_pb7bO9ggV5KU_xeeZRhFSS72OJw/edit?usp=sharing

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

In this live, instructor-led hands-on lab, you’ll learn how to build a modern data stack with Databricks and dbt, using dbt to manage data transformations in Databricks and perform exploratory data analysis on the clean data sets using Databricks SQL. Based on the lakehouse architecture and built on an open data lake, data analysts, analytics engineers, and data scientists can use dbt and Databricks to work with the freshest and most complete data, and quickly derive new insights for accurate decision-making.

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/.

Historically, analytics has been focused on "what happened." And to this day, newer and newer generations of tooling, dbt for example, have come forth accelerating the speed and utility of data in an enterprise for decision making. Machine learning, on the other hand (the "what will happen"), has seemingly been stood up as a separate silo with an organization with seemingly "more intricate" technical requirements, the need for "data scientist", and done so all in the name of how to handle "more special" data resulting in "more accurate" decision making. In this session, you will learn how to cut through the noise and extend and leverage your analytic practice with Snowflake and dbt Labs into the realm of machine learning by pairing your analytical pipelines with a feature store layer to declaratively serve both model training and model scoring scenarios, even at some of the lowest latency (real-time) production requirements.

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/1H-aPsc2DkPGcJUV4pd_iLSdMMMAMZTEtsAFO4hpglXM/edit#slide=id.g15a4510fa6a_0_560

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