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/.

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/.

When Prateek Chawla, founding engineer, joined Monte Carlo in 2019, he was responsible for spinning up our data platform from scratch. He was more of a backend/cloud engineer, but like with any startup had to wear many hats, so got the opportunity to play the role of data engineer too. In this talk, we’ll walk through how we spun up Monte Calro’s data stack with Snowflake, Looker, and dbt, touching on how and why we implemented dbt (and later, dbt Cloud), key use cases, and handy tricks for integrating dbt with other popular tools, like Airflow, and Spark. We’ll discuss what worked, what didn’t work, and other lessons learned along the way, as well as share how our data stack evolved over time to scale to meet the demands of our growing startup. We’ll also touch on a very critical component of the dbt value proposition, data quality testing, and discuss some of our favorite tests and what we’ve done to automate and integrate them with other elements of our stack.

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/.

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/.

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/.

Calling all Snowflake and dbt admins! Come learn how to utilize dbt to manage Snowflake users and network policies and keep your data stack simple and lean while version controlling Snowflake’s RBAC and network policies.

Check the slides here: https://docs.google.com/presentation/d/1IxmDMgVaOYb0PTEWej-k3l0avKrmHI970oT7_Xpg0Z4/edit?usp=sharing

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

Let’s get down to business! Most business users don't want to be bogged down in the data modeling and complexities that us data folk work so hard to accomplish and overcome. Instead, business users and leadership members want the dashboards and numbers they care about. In this session, Matthew Hoss (Element Biosciences) shares his four-step approach to modeling and creating turnkey cost dashboards, all sitting on top of a Netsuite/Fivetran/Snowflake/dbt/Tableau data stack, that help business users get the answers they need, quickly.

Check the slides here: https://docs.google.com/presentation/d/1VVZwm2Kloy1aeewqbB--7WfxIifnpIZflx9V8Q2N-x0/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/.

See how visual data modeling and dbt combine to improve interaction and understanding between analytics engineering practitioners, product owners, and business partners. We will demonstrate conceptual and logical modeling techniques and diagrams to establish common understanding, enhance business partner collaboration, enhance translation of requirements, and ultimately complement analytics engineering within dbt to improve time to value. Demonstrate how to pair data modeling concepts (conceptual, logical, physical) and tools (SqlDBM) to engage your customers and inform the analytics engineering with dbt and Snowflake. We will show how this workbench and tools complement the analytics lifecycle for engineers and data consumers alike. The workbench includes a dbt, a visual modeling tool, and phData Toolkit CLI.

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/1fJhaMGvD7TvVft4nEJYhMRhyanQTw3lbzLrgZFsmj-0/edit?usp=sharing

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