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/.

Should you consider building a Data App?

How many times has your product team asked for data science models to be available in realtime to serve feature flags and product recommendations to customers? They don’t, but they should, and with data apps the data team can make this a reality.

Join TJ Murphy of Multi Media LLC, Kevin Chao from Ramp, and Tejas Manohar from Hightouch to hear examples of data apps in the real world. Their aim is to give data practitioners a framework for when and why to use the warehouse for production applications, and why the data team is the right team for this undertaking.

TJ will walk through the data apps he built at Minted, including a user personalization service and marketing automation tools. At Minted, the data team supported a GraphQL layer on top of the warehouse that supported both web and mobile app personalization on a per user basis.

Kevin Chao will share how Ramp, a fintech leader valued at $8B, is using dbt and Hightouch to power compliance via Snowflake as the source of truth.

Tejas will share how Supr Daily, the Instacart of India, runs product recommendations in their mobile app and automatically sends push notifications at opportune moments to convert users at a higher rate.

Lastly, TJ will give a practical overview of architecture, and a checklist of what to think through before building a Data App.

Check the slides here: https://docs.google.com/presentation/d/1LMuuuvVy3QD2ZAltp5c1Eh5Ik4LgM0q-AMlThsZVR40/edit#slide=id.g166573b6b47_0_0

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/.

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/.

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/.

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/.