Coalesce 2021

Companies today need a product analytics platform to enable product insights that are quick to access and easy to understand and act upon. How can you prepare?

A story of how 2 motivated actuaries implemented a solution to fix our broken actuarial data pipelines using the dbt CLI, without engineering or developers.

Drew Banin 8 Dec 2021

While dbt's open source roots has always made this much easier, we believe in a world where the entire analytics ecosystem grows with us, from Core, to Cloud, and beyond.

This session is a deep dive into three ways the dbt Cloud API can be used to foster a culture of greater awareness: through real time monitoring, impact analysis, and historical understanding.

We believe the future of analytics workflows is polyglot and will embrace better interoperability between languages and UIs.

Astasia Myers | Jennifer Li | Julia Schottenstein | Sarah Catanzaro 8 Dec 2021

A panel discussion about trends in the data space. Hear from venture capitalists on what's new and interesting in the ecosystem.

For most people, the modern data stack isn’t a collection of architectural diagrams; it’s an experience.

Jeremy Cohen 8 Dec 2021

Jeremy will offer a highlight reel of the biggest changes included in dbt v1.0, as well as honorable mentions for some neat ideas that ended up on the cutting-room floor.

Databricks, Delta Lake, & DBT provide the perfect trio for saving investment management firms money and boosting productivity by allowing analysts to deploy SQL pipelines much faster.

In this talk, we will introduce Deepnote, a collaborative data science notebook and how you can use it with dbt to introduce more collaboration into your team’s data modelling and exploratory workflows.

Jason Ganz 7 Dec 2021

This talk is about envisioning the world of five years from now when every organization has an Analytics Engineering department.

In this talk we will demonstrate how to get started with streaming analytics using the same analytics engineering skillset that keeps you productive in batch.

If you are trying to figure out how one builds a diverse team, do not ask your black co-worker how you should go about doing it.

This talk is about how modern data teams working in the “data as product” and “analytics engineering” models can benefit from using prototyping practices.

Delivering high impact, high velocity, high quality data products in highly volatile data contexts.

Emilie Schario 7 Dec 2021

I propose we lobby against the title Data Scientist, instead choosing some variation of the Core Four Data Roles: Data Analyst, Analytics Engineer, Data Engineer, and Machine Learning Engineer.

How I was able to adopt dbt coming from an industrial engineering background, and how it can be leveraged by end users with a quality control mindset to solve common business problems.

Have you ever felt that you were not the intended audience of the technology you consume? Are there software design paradigms that simply do not work for you?

Using dbt artifacts we were able to create a central monitoring and alerting system that notified people if a specific dbt model failed.

I will illustrate these ideas with a demonstration of the {dbtplyr} dbt package which helps analytics engineers get the most value from controlled vocabularies by making it easier to effectively exploit column naming structures while coding.

How my team decreased query time in Looker and saved Snowflake credits, by materializing views and tables and implementing tags for job schedules.

Hiring is a critical point in influencing company and team culture and for bringing on folks who can really get the job done.

Attendees will walk away with some recommendations for creating their own data product strategy that will be useful no matter what scale they are operating on.

A dbt package is not only a way to make a particular process easier or more robust or more efficient – it can also be a way to productionize/share practices.

One of our biggest wins so far has been finding clever ways to incrementalize our longest-running tables, while still solving for 2 main pain points.

We'll show our method to automate source transformations. Aula's growth and a proliferation of nonrelational sources required constant manual maintenance to our dbt project -- which was sucking up the data team's capacity.

This session will cover our own analytical use cases, as well as features Fivetran is building to support dbt in the open-source community, like our Visual Studio Code integration, Fivetran-managed dbt packages and our Transformations product line.

Learn what questions you should be asking before seeking out consultants, what a successful engagement should look like, and how to align your internal talent with external strengths that best serve your team.

Claire Carroll 6 Dec 2021

Together, we’ll demystify the git flow and build a mental model for how git works. You’ll leave the talk feeling ready to use git on every project you work on!

Ever wondered how to build up a dbt project from the ground up? How do you know when good enough is good enough?

As data analytics becomes a cornerstone for modern companies, understanding who owns what in the data organization has never been more confusing—or important.

Martin Casado | Tristan Handy 6 Dec 2021

The modern data stack is the third generation of data analysis products to come to prominence since the 90's.

Investing in analytics engineering was a game-changer for our ability to deliver value as a data organization, but it didn't happen overnight.

Erica Louie 6 Dec 2021

This is an overview of how our Data team is structured, how we interact with the greater org, the general operations, and our expectations/responsibilities.

Rosie Sampaio Cardoso 6 Dec 2021

Are you interested in learning more about our dbt Meetups, or how to become a Meetup organizer in your community?

In this panel, we'll bring on an assortment of thought leaders and data practitioners to discuss how and why companies are using cloud data warehouses for operations today.

Drew Banin | Reynold Xin 6 Dec 2021

Reynold Xin is a technical co-founder and Chief Architect at Databricks. He’s also a co-creator and the top contributor to the Apache Spark project.

Alexis Wong Baird | Jonathan Natkins 6 Dec 2021

If dbt is new to you, join us in this session to get up to speed. We'll go through the basics and give you all the information you need to get the most value out of this conference!