Understanding the user funnel and measuring conversion is critical to Acorns as a subscription business. The engineering team turned to Rudderstack to track customer interaction in near real-time across web, ios, and android. However, transforming that into actionable insights required carefully curated SQL spanning two datastores. Come learn how the data engineering team used dbt to build a centralized metrics interface and dynamic funnels in a data landscape spanning Rudderstack, Redshift, Databricks, and dbt with Tableau as our visualization tool.

Check the slides here: https://docs.google.com/presentation/d/1MTbqysGH_9oxUPKgQQO2MYM1f1XUhSVw_ERDvaZ8Qsg/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 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/.

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/.

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/.

Creating / migrating metric metadata to dbt can be a pain, because the level of underlying data knowledge required to create the YAML files properly. You might have found yourself wondering, “is this worth it just to standardize metric definitions?”. This talk will tell you why it is definitely worth it… because the functionality you unlock goes beyond just standard metric definitions. Adopting the dbt standard metric syntax unlocks three additional possibilities for your data:

1. Automated time-aware metric calculations

2. Dynamic drill downs and segmentation to empower slice and dice analysis

3. Self-service dynamic transforms using templated SQL

Check slides here: https://docs.google.com/presentation/d/1nJHP2E6NGZ-KHG4_gNiI6w2lq4kjWgQIanAC9yf3cng

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

In this workshop, you’ll learn how to create and document macros that leverage the powerful introspective features of dbt to perform dynamic modeling including: run result storage in your warehouse, dynamic value lookup in models, and leveraging model metadata in macros.

You’ll learn how to: - Create macros to store your dbt run results within your data warehouse - Leverage internal dbt graph data for dynamic modeling - Incorporate dbt best-practices when developing macros

Prerequisites: - Basic familiarity with ANSI SQL - Some familiarity using Jinja and writing macros - Experience with dbt required

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

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

For the first time in dbt, you can now run Python models, making it possible to supplement the accessibility of SQL with a new level of power and flexibility.

When is it useful to use Python, and when should you stick with SQL instead? What might a multilingual dbt project look like in practice, and what could it make possible for your team?

Join Jeremy Cohen, Cody Peterson, and Leah Antkiewicz to explore these questions in this interactive session.

Check the slides here: https://docs.google.com/presentation/d/1e3wB7EQ0EXugGhfCjVCp_dDFEbY_uKyVjMqG1o7alnA/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/.

Is your SQL query the problem, or how you ask for the data you need, when you need it. In this deep dive, Jake Thomas shares his hypothesis for why the jsonschema is the ticket to contract-driven communication, system interoperability, and an overall improvement to data processing quality of life.

Check the slides here: https://docs.google.com/presentation/d/1kiGyQF7NUWfx-5RyIyeEwSUCwqtIdrXADeI2iixUgiI/edit?usp=sharing

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/.

Description: dbt now supports Python models! In this hands-on workshop you’ll learn how to build your first Python models in dbt, alongside SQL at the center of your transformations.

You’ll learn how to: - Build your Python transformation in a notebook - Add this transformation as a model in your dbt project - Decide between building models in SQL or in Python

Prerequisites: - Basic familiarity with Python and DataFrames - If you want to use your own Warehouse and dbt project, make sure that you have dbt 1.3 installed and have followed the “additional setup” from our docs

Check the slides here: https://docs.google.com/presentation/d/133CVwwAxc5qT80ZJwngQ_ZSikOkCttvzWwGpdZCgOHQ/edit#slide=id.g1693e59a4f4_0_0

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/.