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/.

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/.

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/.

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/.

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/.