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/.

Python model support is one of the hottest topics in the dbt Community Slack right now—but also one of the most intangible. What’s possible? What’s practical? What’s the point? In this session, Eda Johnson (Snowflake) will show how Snowpark Python further enhances a dbt + Snowflake development experience by supporting new workloads, including Machine Learning within your dbt DAGs.

Check the slides here: https://docs.google.com/presentation/d/1IJSeE96bze7DECuDYqsTVv6FaOcNcJ5tTiCWKEku_QQ/edit#slide=id.g14034313943_0_36

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/.

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/.

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/.

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/.

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/.