Invite-only, more information to come!&nbsp;

San Diego

dbt Labs partner day 

Partner day

This is the introductory course for developers jumping into dbt! We will cover the same concepts from the on demand fundamentals course including models, sources, tests, docs, and deployment. We will also introduce the concepts of semantic layer and multi-project. As an instructor led course, you’ll have the chance to learn with peers, ask questions, and get live coaching and feedback. Learning objectives: After this course you will be able to: 

- Explain the core concepts of dbt 

- Build data models and a DAG to visualize dependencies 

- Configure tests and add documentation to your models 

- Deploy your dbt project to refresh data models on a schedule 

Prerequisites: The prerequisites for this course include: intermediate SQL knowledge 

What to bring: You will need to bring your own laptop to complete the hands-on exercises. We will provide all the other sandbox environments for dbt Cloud and data platform.

This is the introductory course for developers jumping into dbt! We will cover the same concepts from the on demand fundamentals course including models, sources, tests, docs, and deployment. We will also introduce the concepts of semantic layer and multi-project. As an instructor led course, you’ll have the chance to learn with peers, ask questions, and get live coaching and feedback. Learning objectives: After this course you will be able to:&nbsp;- Explain the core concepts of dbt&nbsp;- Build data models and a DAG to visualize dependencies&nbsp;- Configure tests and add documentation to your models&nbsp;- Deploy your dbt project to refresh data models on a schedule&nbsp;Prerequisites: The prerequisites for this course include: intermediate SQL knowledge&nbsp;What to bring: You will need to bring your own laptop to complete the hands-on exercises. We will provide all the other sandbox environments for dbt Cloud and data platform.

Training: Getting started with dbt

Training

This is the introductory course for developers jumping into dbt! We will cover the same concepts from the on demand fundamentals course including models, sources, tests, docs, and deployment. We will...

Get certified at Coalesce! Choose from one of two certifications: dbt Developer and dbt Cloud Administrator. Your purchase includes sitting for one attempt for one of the two exams in person at Coalesce. Both exams are 2 hours in length, proctored in person, and you will need to bring your own computer to take the exam.

Get certified at Coalesce! Choose from one of two certifications: dbt Developer and dbt Cloud Administrator. Your purchase includes sitting for one attempt for one of the two exams in person at Coalesce. Both exams are 2 hours in length, proctored in person, and you will need to bring your own computer to take the exam.

Certification

Get certified at Coalesce! Choose from one of two certifications: dbt Developer and dbt Cloud Administrator. Your purchase includes sitting for one attempt for one of the two exams in person at...

San Diego - Welcome happy hour

Class: "Unwind" Slow Flow

Participants will be guided through a grounded breath work and a gentle seated and standing yoga sequence to allow the body to relax. This is a great option for those who are new to yoga.

Mats will be provided. 

Class: "Unwind" Slow FlowParticipants will be guided through a&nbsp;grounded breath work and a gentle seated&nbsp;and standing yoga sequence to allow the&nbsp;body to relax. This is a great option for those&nbsp;who are new to yoga.Mats will be provided.

"Unwind" slow flow — brought to you by Census 

Class: "Unwind" Slow Flow

Participants will be guided through a grounded breath work and a gentle seated and standing yoga sequence to allow the body to relax. This is a great option for those who...

Kick off your Coalesce adventure at our Keynote opener and Product Spotlight. Tristan Handy, dbt Labs CEO, will share his vision for the future, and will discuss the exciting new developments you can expect in the coming year. There will also be dbt enthusiasts sharing the incredible things they're doing with dbt Cloud, and so much more! It's the perfect way to get energized for your week ahead and our chance to welcome you to the magic of Coalesce.

Kick off your Coalesce adventure at our Keynote opener and Product Spotlight. Tristan Handy, dbt Labs CEO, will share his vision for the future, and will discuss the exciting new developments you can expect in the coming year. There will also be dbt enthusiasts sharing the incredible things they're doing with dbt Cloud, and so much more! It's the perfect way to get energized for your week ahead and our chance to welcome you to the magic of Coalesce.

Vice President, Product

dbt Labs product spotlight & keynote

Keynote

Kick off your Coalesce adventure at our Keynote opener and Product Spotlight. Tristan Handy, dbt Labs CEO, will share his vision for the future, and will discuss the exciting new developments you can...

With the dbt Semantic Layer, you can define metrics alongside your dbt models, and access them from any integrated analytics tool. In this workshop, you’ll learn to set up your first metrics in dbt following best practices, getting them ready for downstream analysis.

With the dbt Semantic Layer, you can define metrics alongside your dbt models, and access them from any integrated analytics tool. In this workshop, you’ll learn to set up your first metrics in dbt following best practices, getting them ready for downstream analysis.

An introduction to the dbt Semantic Layer

Workshop

With the dbt Semantic Layer, you can define metrics alongside your dbt models, and access them from any integrated analytics tool. In this workshop, you’ll learn to set up your first metrics in dbt...

dbt has been adopted as an industry standard, now used by more than 20,000 organizations. For larger companies with more complex deployments, however, it can still be a challenge to sustain efficient collaboration around data development without sacrificing governance. Here is how dbt Cloud is helping to overcome that challenge.

dbt has been adopted as an industry standard, now used by more than 20,000 organizations. For larger companies with more complex deployments, however, it can still be a challenge to sustain efficient collaboration around data development without sacrificing governance. Here is how dbt Cloud is helping to overcome that challenge.

Product Manager

Scaling collaboration with dbt Cloud

Product tour

dbt has been adopted as an industry standard, now used by more than 20,000 organizations. For larger companies with more complex deployments, however, it can still be a challenge to sustain efficient...

It’s 2023, why are software engineers still breaking analytics reporting? We’ve all been there, being alerted by an analyst or C-level stakeholders, saying “this report is broken”, only to spend hours determining that an engineer deleted a column on the source database that is now breaking your pipeline and reporting.
 
At Xometry, the data engineering team, wanted to fix this problem at its core and give the engineering teams a clear and repeatable process that allowed them to be the owners of their own database data. Xometry named the process DPICT (data pipeline contract) and built several internal tools that integrated seamlessly with their developer’s microservice toolsets.
 
Their software engineers mostly build their database microservices using Postgres, and bring in the data using Fivetran. Using that as our baseline, the team created a set of tools that would allow the engineers to quickly build the staging layer of their database in the data warehouse (Snowflake) but also alert them of the consequences of removing a table or column in downstream reporting.


In this talk, Jisan will share with you the nuts and bolts of the designed solution and process that allowed the team to onboard 13 different microservices seamlessly, working with multiple domains and dozens of developers. The process also helped software engineers to own their own data and realize their impact. The team has saved hundreds hours of data engineering time and resources not having to chase down what changed upstream to break data. Overall, this process has helped to bring transparency to the whole data ecosystem.

<span data-sheets-userformat='{"2":15293,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"10":2,"11":4,"12":0,"14":{"1":2,"2":0},"15":"Calibri","16":11}' data-sheets-value='{"1":2,"2":"It’s 2023, why are software engineers still breaking analytics reporting? We’ve all been there, being alerted by an analyst or C-level stakeholders, saying “this report is broken”, only to spend hours determining that an engineer deleted a column on the source database that is now breaking your pipeline and reporting.\n \n\n At Xometry, we, the data engineering team, wanted to fix this problem at its core and wanted to give our engineering teams a clear and repeatable process that allowed them to be the owners of their own database data. We named our process DPICT (data pipeline contract) and built several internal tools that integrated seamlessly with our developer’s microservice toolsets.\n \n\n Our software engineers mostly build their database microservices using Postgres, and we bring in the data using Fivetran. Using that as our baseline, we created a set of tools that would allow the engineers to quickly build the staging layer of their database in our data warehouse (Snowflake) but also alert them of the consequences of removing a table or column in downstream reporting.\n \n\n In this talk, I will share with you the nuts and bolts of our designed solution and process that allowed us to onboard 13 different microservices seamlessly, working with multiple domains and dozens of developers. Our process also helped software engineers to own their own data and realize their impact. We’ve also saved hundreds hours of data engineering time and resources not having to chase down what changed upstream to break data. Overall, this process has helped to bring transparency to our whole data ecosystem."}'>It’s 2023, why are software engineers still breaking analytics reporting? We’ve all been there, being alerted by an analyst or C-level stakeholders, saying “this report is broken”, only to spend hours determining that an engineer deleted a column on the source database that is now breaking your pipeline and reporting. At Xometry, the data engineering team, wanted to fix this problem at its core and give the engineering teams a clear and repeatable process that allowed them to be the owners of their own database data. Xometry named the process DPICT (data pipeline contract) and built several internal tools that integrated seamlessly with their developer’s microservice toolsets. Their software engineers mostly build their database microservices using Postgres, and bring in the data using Fivetran. Using that as our baseline, the team created a set of tools that would allow the engineers to quickly build the staging layer of their database in the data warehouse (Snowflake) but also alert them of the consequences of removing a table or column in downstream reporting. <span data-sheets-userformat='{"2":15293,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"10":2,"11":4,"12":0,"14":{"1":2,"2":0},"15":"Calibri","16":11}' data-sheets-value='{"1":2,"2":"It’s 2023, why are software engineers still breaking analytics reporting? We’ve all been there, being alerted by an analyst or C-level stakeholders, saying “this report is broken”, only to spend hours determining that an engineer deleted a column on the source database that is now breaking your pipeline and reporting.\n \n\n At Xometry, we, the data engineering team, wanted to fix this problem at its core and wanted to give our engineering teams a clear and repeatable process that allowed them to be the owners of their own database data. We named our process DPICT (data pipeline contract) and built several internal tools that integrated seamlessly with our developer’s microservice toolsets.\n \n\n Our software engineers mostly build their database microservices using Postgres, and we bring in the data using Fivetran. Using that as our baseline, we created a set of tools that would allow the engineers to quickly build the staging layer of their database in our data warehouse (Snowflake) but also alert them of the consequences of removing a table or column in downstream reporting.\n \n\n In this talk, I will share with you the nuts and bolts of our designed solution and process that allowed us to onboard 13 different microservices seamlessly, working with multiple domains and dozens of developers. Our process also helped software engineers to own their own data and realize their impact. We’ve also saved hundreds hours of data engineering time and resources not having to chase down what changed upstream to break data. Overall, this process has helped to bring transparency to our whole data ecosystem."}'>In this talk, Jisan will share with you the nuts and bolts of the designed solution and process that allowed the team to onboard 13 different microservices seamlessly, working with multiple domains and dozens of developers. The process also helped software engineers to own their own data and realize their impact. The team has saved hundreds hours of data engineering time and resources not having to chase down what changed upstream to break data. Overall, this process has helped to bring transparency to the whole data ecosystem.

Data Engineering Manager

Using data pipeline contract to prevent breakage in analytics reporting

Modernization & migration

It’s 2023, why are software engineers still breaking analytics reporting? We’ve all been there, being alerted by an analyst or C-level stakeholders, saying “this report is broken”, only to spend...

In this session, the team at Rocket Money will explain how they built a Quote-to-Cash (Q2C) system in dbt Cloud that the accounting team uses for end-of-month close. This was also used to pass Rocket Money's first SOX audit with zero deficiencies. The presentation will review how the team gathered requirements (for different products, payment providers, etc.) and built and tested all of their models.

Staff Analytics Engineer

Quote-to-Cash: Automating accounting's end-of-month close process (and passing our first SOX audit)

Business critical data teams

In this session, the team at Rocket Money will explain how they built a Quote-to-Cash (Q2C) system in dbt Cloud that the accounting team uses for end-of-month close. This was also used to pass Rocket...

As Joe Reis recently opined, if you want to know what’s next in data engineering, just look at the software engineer. The MDS-in-a-box pattern has been a game changer for applying software engineering principles to local data development– improving the ability to share data, collaborate on modeling work and data analysis the same way we build and share open source tooling.

This panel brings together experts in data engineering, data analytics and software engineering to explore the current state of the pattern, pieces that remain missing today and how emerging tools and data engineering testing capabilities can refine the transition from local development to production workflows.


<span data-sheets-userformat='{"2":9021,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"11":4,"12":0,"16":11}' data-sheets-value='{"1":2,"2":"Description\nAs Joe Reis recently opined, if you want to know what’s next in data engineering, just look at the software engineer. The MDS-in-a-box pattern has been a game changer for applying software engineering principles to local data development– improving the ability to share data, collaborate on modeling work and data analysis the same way we build and share open source tooling.\n\nThis panel brings together experts in data engineering, data analytics and software engineering to explore the current state of the pattern, pieces that remain missing today and how emerging tools and data engineering testing capabilities can refine the transition from local development to production workflows.\n\n"}'>As Joe Reis recently opined, if you want to know what’s next in data engineering, just look at the software engineer. The MDS-in-a-box pattern has been a game changer for applying software engineering principles to local data development– improving the ability to share data, collaborate on modeling work and data analysis the same way we build and share open source tooling. This panel brings together experts in data engineering, data analytics and software engineering to explore the current state of the pattern, pieces that remain missing today and how emerging tools and data engineering testing capabilities can refine the transition from local development to production workflows.

Panel discussion: Fixing the data eng lifecycle 

As Joe Reis recently opined, if you want to know what’s next in data engineering, just look at the software engineer. The MDS-in-a-box pattern has been a game changer for applying software...

TOCA Football, the largest Operator of Indoor Soccer Centers in North America, leverages accurate data to power analytics for over 30 training centers, providing everything from operational insights for executives to ball-by-ball analysis. In 2020, the team adopted a cloud-native data stack with dbt to scale analytics enablement for the go-to-market org, including the company’s finance, strategy, operations, and marketing teams. By 2022, their lean team of four was struggling to gain visibility into the health and performance of their dbt models. So, what was the TOCA team to do? Two words: data observability.
 
In this talk, Sam Cvetkovski, Director, Data & Analytics will discuss how TOCA built their larger data observability strategy to reduce model bloat, increase data accuracy, and boost stakeholder satisfaction with their team’s data products. She’ll share her biggest “aha!” moments, key challenges, and best practices for teams getting started on their dbt reliability journeys.

<span data-sheets-userformat='{"2":15293,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"10":2,"11":4,"12":0,"14":{"1":2,"2":0},"15":"Calibri","16":11}' data-sheets-value='{"1":2,"2":"TOCA Football, the largest Operator of Indoor Soccer Centers in North America, leverages accurate data to power analytics for over 30 training centers, providing everything from operational insights for executives to ball-by-ball analysis. In 2020, the team adopted a cloud-native data stack with dbt to scale analytics enablement for the go-to-market org, including the company’s finance, strategy, operations, and marketing teams. By 2022, their lean team of four was struggling to gain visibility into the health and performance of their dbt models. So, what was the TOCA team to do? Two words: data observability.\n \n\n In this talk, Sam Cvetkovski, Director, Data & Analytics will discuss how TOCA built their larger data observability strategy to reduce model bloat, increase data accuracy, and boost stakeholder satisfaction with their team’s data products. She’ll share her biggest “aha!” moments, key challenges, and best practices for teams getting started on their dbt reliability journeys."}'>TOCA Football, the largest Operator of Indoor Soccer Centers in North America, leverages accurate data to power analytics for over 30 training centers, providing everything from operational insights for executives to ball-by-ball analysis. In 2020, the team adopted a cloud-native data stack with dbt to scale analytics enablement for the go-to-market org, including the company’s finance, strategy, operations, and marketing teams. By 2022, their lean team of four was struggling to gain visibility into the health and performance of their dbt models. So, what was the TOCA team to do? Two words: data observability. In this talk, Sam Cvetkovski, Director, Data &amp; Analytics will discuss how TOCA built their larger data observability strategy to reduce model bloat, increase data accuracy, and boost stakeholder satisfaction with their team’s data products. She’ll share her biggest “aha!” moments, key challenges, and best practices for teams getting started on their dbt reliability journeys.

Data is life: How TOCA football keeps their eye on the ball with dbt and data observability

dbt at scale

TOCA Football, the largest Operator of Indoor Soccer Centers in North America, leverages accurate data to power analytics for over 30 training centers, providing everything from operational insights...

New constructs in dbt make it simple to break up your monolothic dbt project into multiple smaller projects, with clear interfaces and robust governance. Learn to set up your own multi-project dbt deployment following best practices in this interactive workshop.

New constructs in dbt make it simple to break up your monolothic dbt project into multiple smaller projects, with clear interfaces and robust governance. Learn to set up your own multi-project dbt deployment following best practices in this interactive workshop.

Beyond the monolith: Multi-project approaches

New constructs in dbt make it simple to break up your monolothic dbt project into multiple smaller projects, with clear interfaces and robust governance. Learn to set up your own multi-project dbt...

Analytics Engineer

Peer exchange: Defining role boundaries in big orgs

Peer exchange

In this session, we will highlight how dbt plays a key role in MuleSoft’s modern data analytics framework. As MuleSoft continues to grow, the data platform engineering team encountered some challenges with the data transformations. The session will dive deeper into the specific challenges faced by the team and demonstrate how dbt’s features including CI/CD, testing, documentation and lineage help overcome these challenges.

In this session, we will highlight how dbt plays a key role in MuleSoft’s modern data analytics framework. As MuleSoft continues to grow, the data platform engineering team encountered some challenges with the data transformations. The session will dive deeper into the specific challenges faced by the team and demonstrate how dbt’s features including CI/CD, testing, documentation and lineage help overcome these challenges.

Data Platform Engineer

Solution Architect

Powering the MuleSoft (a Salesforce Company) modern data analytics framework with dbt

In this session, we will highlight how dbt plays a key role in MuleSoft’s modern data analytics framework. As MuleSoft continues to grow, the data platform engineering team encountered some...

In this session, Vida Health’s senior director of data, mobile, and web engineering will share a story that can help other data and business leaders capitalize on the opportunities being created by current technology innovations, market realities, and real-world problems. This includes a playbook on how Vida Health uses modern data technologies like dbt Cloud, Fivetran, Looker, BigQuery, BigQueryML/dbtML, Vertex AI, LLMs, and more to put data in the driver’s seat to solve meaningful problems in complex industries like healthcare.

In this session, Vida Health’s senior director of data, mobile, and web engineering will share a story that can help other data and business leaders capitalize on the opportunities being created by current technology innovations, market realities, and real-world problems. This includes a playbook on how Vida Health uses modern data technologies like dbt Cloud, Fivetran, Looker, BigQuery, BigQueryML/dbtML, Vertex AI, LLMs, and more to put data in the driver’s seat to solve meaningful problems in complex industries like healthcare.

Senior Director, Data and Frontend Engineering

Transforming healthcare by putting data in the driver’s seat at Vida Health

In this session, Vida Health’s senior director of data, mobile, and web engineering will share a story that can help other data and business leaders capitalize on the opportunities being created by...

With the dbt Semantic Layer, you can define metrics alongside your dbt models, and access them from any integrated analytics tool. 

The Semantic Layer has been totally revamped. It’s now powered by MetricFlow, allowing for more complex metric definition and a significantly improved querying experience. Join the dbt Labs product team as they dive into what’s new, what’s different, and what it means for you.

With the dbt Semantic Layer, you can define metrics alongside your dbt models, and access them from any integrated analytics tool. The Semantic Layer has been totally revamped. It’s now powered by MetricFlow, allowing for more complex metric definition and a significantly improved querying experience. Join the dbt Labs product team as they dive into what’s new, what’s different, and what it means for you.

Director, Product Management

The new-look dbt Semantic Layer, powered by MetricFlow

With the dbt Semantic Layer, you can define metrics alongside your dbt models, and access them from any integrated analytics tool. 

The Semantic Layer has been totally revamped. It’s now powered by...

This is the introductory course for developers jumping into dbt! We will cover the same concepts from the on demand fundamentals course including models, sources, tests, docs, and deployment. We will also introduce the concepts of semantic layer and multi-project. As an instructor led course, you’ll have the chance to learn with peers, ask questions, and get live coaching and feedback.
- learning objectives: After this course you will be able to:
    - Explain the core concepts of dbt
    - Build data models and a DAG to visualize dependencies
    - Configure tests and add documentation to your models
    - Deploy your dbt project to refresh data models on a schedule
- prerequisites: The prerequisites for this course include:
    - intermediate SQL knowledge
- what to bring: You will need to bring your own laptop to complete the hands-on exercises. We will provide all the other sandbox environments for dbt Cloud and data platform.

This is the introductory course for developers jumping into dbt! We will cover the same concepts from the on demand fundamentals course including models, sources, tests, docs, and deployment. We will also introduce the concepts of semantic layer and multi-project. As an instructor led course, you’ll have the chance to learn with peers, ask questions, and get live coaching and feedback. - learning objectives: After this course you will be able to: &nbsp; &nbsp;- Explain the core concepts of dbt &nbsp; &nbsp;- Build data models and a DAG to visualize dependencies &nbsp; &nbsp;- Configure tests and add documentation to your models &nbsp; &nbsp;- Deploy your dbt project to refresh data models on a schedule - prerequisites: The prerequisites for this course include: &nbsp; &nbsp;- intermediate SQL knowledge - what to bring: You will need to bring your own laptop to complete the hands-on exercises. We will provide all the other sandbox environments for dbt Cloud and data platform.

Tips and tricks on how La-Z-Boy uses a modern data stack of Fivetran and dbt to modernize their infrastructure and tackle complex retail and manufacturing data problems. Hear from a data leader about their experience and how you can replicate their success. And start your session off with some comfortable data movements to make sure you can extract as much information as possible from our loaded session on data transformation.

<span data-sheets-userformat='{"2":15293,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"10":2,"11":4,"12":0,"14":{"1":2,"2":0},"15":"Calibri","16":11}' data-sheets-value='{"1":2,"2":"Tips and tricks on how La-Z-Boy uses a modern data stack of Fivetran and dbt to modernize their infrastructure and tackle complex retail and manufacturing data problems. Hear from a data leader about their experience and how you can replicate their success. And start your session off with some comfortable data movements to make sure you can extract as much information as possible from our loaded session on data transformation."}'>Tips and tricks on how La-Z-Boy uses a modern data stack of Fivetran and dbt to modernize their infrastructure and tackle complex retail and manufacturing data problems. Hear from a data leader about their experience and how you can replicate their success. And start your session off with some comfortable data movements to make sure you can extract as much information as possible from our loaded session on data transformation.

Lead Product Marketing Manager

Director of Enterprise Architecture

Move data comfortably: Modernizing your infrastructure with La-Z-Boy and Fivetran

Tips and tricks on how La-Z-Boy uses a modern data stack of Fivetran and dbt to modernize their infrastructure and tackle complex retail and manufacturing data problems. Hear from a data leader about...

Since its inception as Sinter, dbt Cloud has come a long way in a short time. As its breathless evolution continues unabated, we take a moment to pause and reflect. What have we learned so far? And where do we go next?

Product talk: Perspectives & possibilities

Since its inception as Sinter, dbt Cloud has come a long way in a short time. As its breathless evolution continues unabated, we take a moment to pause and reflect. What have we learned so far? And...

Census GOLD Session

With dbt and the modern data stack, onboarding and surfacing data has never been more manageable for analytics engineers. Still, 85% of data products never make it into production. Why do data practitioners struggle to create data products that engage the people who actually use them?

Join Jake and Nate as they discuss how integration and collaboration can drive user engagement, ultimately leading to increased adoption. They’ll present guiding principles and three real world examples of data products designed to center and empower business users to:
- Replace the need for seeds using materialized Sigma input tables
- Create and manage HubSpot contacts and segments using Sigma’s HubSpot template
- Sync insights from Sigma directly to downstream systems using the Hightouch integration.

The lessons learned from building a data platform at Sigma will provide everyone a framework for fostering collaboration between data teams and business users so they can raid insights, move up the leaderboard, and level up their gameplay no matter what data stack they use.

<span data-sheets-userformat='{"2":961,"3":{"1":0},"9":0,"10":1,"11":4,"12":0}' data-sheets-value='{"1":2,"2":"With dbt and the modern data stack, onboarding and surfacing data has never been more manageable for analytics engineers. Still, 85% of data products never make it into production. Why do data practitioners struggle to create data products that engage the people who actually use them?\n\nJoin Jake and Nate as they discuss how integration and collaboration can drive user engagement, ultimately leading to increased adoption. They’ll present guiding principles and three real world examples of data products designed to center and empower business users to:\n- Replace the need for seeds using materialized Sigma input tables\n- Create and manage HubSpot contacts and segments using Sigma’s HubSpot template\n- Sync insights from Sigma directly to downstream systems using the Hightouch integration.\n\nThe lessons learned from building a data platform at Sigma will provide everyone a framework for fostering collaboration between data teams and business users so they can raid insights, move up the leaderboard, and level up their gameplay no matter what data stack they use."}'>With dbt and the modern data stack, onboarding and surfacing data has never been more manageable for analytics engineers. Still, 85% of data products never make it into production. Why do data practitioners struggle to create data products that engage the people who actually use them? Join Jake and Nate as they discuss how integration and collaboration can drive user engagement, ultimately leading to increased adoption. They’ll present guiding principles and three real world examples of data products designed to center and empower business users to: - Replace the need for seeds using materialized Sigma input tables - Create and manage HubSpot contacts and segments using Sigma’s HubSpot template - Sync insights from Sigma directly to downstream systems using the Hightouch integration. The lessons learned from building a data platform at Sigma will provide everyone a framework for fostering collaboration between data teams and business users so they can raid insights, move up the leaderboard, and level up their gameplay no matter what data stack they use.

Director of Partner Engineering, Sigma Computing

Game on: Building massive multiplayer online data products

With dbt and the modern data stack, onboarding and surfacing data has never been more manageable for analytics engineers. Still, 85% of data products never make it into production. Why do data...

We’ve been there before: a small code change leads to breaking countless downstream dbt models and reports in your BI tool. But you followed your team’s development and deployment guidelines: implementing small code changes, running successful dbt tests, and getting your PR reviewed by other team members. So where was the breakdown?
 
 dbt tests and packages establish the baseline of your data quality with assertion-based testing; we’re going beyond that and making the case to bring regression testing—the original software engineering testing practice—to your dbt workflows with data diffing. In this session, we’ll go deep on why data teams need data diffing to implement regression testing and find data quality issues before they enter your production pipelines.

 Specifically, we’ll share:
 1. Why your data quality assurance has gaping holes in the absence of regression testing,
 2. How Datafold’s data diffing capabilities differ from assertion-based testing and find typically unforeseen data quality issues using regression testing,
 3. How to quickly implement data diffing in your dbt project’s development and CI processes, and
 4. How to democratize data quality testing insights.
 
 All of which enable your data team to move faster without (the fear of) breaking anything 😉.

<span data-sheets-userformat='{"2":15293,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"10":2,"11":4,"12":0,"14":{"1":2,"2":0},"15":"Calibri","16":11}' data-sheets-value='{"1":2,"2":"We’ve been there before: a small code change leads to breaking countless downstream dbt models and reports in your BI tool. But you followed your team’s development and deployment guidelines: implementing small code changes, running successful dbt tests, and getting your PR reviewed by other team members. So where was the breakdown?\n \n\n dbt tests and packages establish the baseline of your data quality with assertion-based testing; we’re going beyond that and making the case to bring regression testing—the original software engineering testing practice—to your dbt workflows with data diffing. In this session, we’ll go deep on why data teams need data diffing to implement regression testing and find data quality issues before they enter your production pipelines.\n \n\n Specifically, we’ll share:\n 1. Why your data quality assurance has gaping holes in the absence of regression testing,\n 2. How Datafold’s data diffing capabilities differ from assertion-based testing and find typically unforeseen data quality issues using regression testing,\n 3. How to quickly implement data diffing in your dbt project’s development and CI processes, and\n 4. How to democratize data quality testing insights.\n \n\n All of which enable your data team to move faster without (the fear of) breaking anything 😉."}'>We’ve been there before: a small code change leads to breaking countless downstream dbt models and reports in your BI tool. But you followed your team’s development and deployment guidelines: implementing small code changes, running successful dbt tests, and getting your PR reviewed by other team members. So where was the breakdown? dbt tests and packages establish the baseline of your data quality with assertion-based testing; we’re going beyond that and making the case to bring regression testing—the original software engineering testing practice—to your dbt workflows with data diffing. In this session, we’ll go deep on why data teams need data diffing to implement regression testing and find data quality issues before they enter your production pipelines. Specifically, we’ll share: 1. Why your data quality assurance has gaping holes in the absence of regression testing, 2. How Datafold’s data diffing capabilities differ from assertion-based testing and find typically unforeseen data quality issues using regression testing, 3. How to quickly implement data diffing in your dbt project’s development and CI processes, and 4. How to democratize data quality testing insights. &nbsp;All of which enable your data team to move faster without (the fear of) breaking anything 😉.

Data diffing: The key to data quality and peace of mind

We’ve been there before: a small code change leads to breaking countless downstream dbt models and reports in your BI tool. But you followed your team’s development and deployment guidelines:...

Fanduel, America's leading sports betting company, migrated all of its data pipelines to integrate with dbt Cloud in order to make data transformation processes more reliable, scalable, and maintainable.

In this presentation, the Fanduel team will share the challenges faced during the migration, including overcoming the inertia of established data infrastructure, integrating with legacy systems, and ensuring a smooth transition without disrupting ongoing data operations. The migration process fostered newfound collaboration among data analysts, product owners, and engineers, with the added benefit of accommodating future business needs.

<span data-sheets-formula="='San Diego - approved'!R[0]C[6]" data-sheets-userformat='{"2":256,"11":4}' data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Fanduel, America's leading sports betting company, migrated all of its data pipelines to integrate with dbt Cloud in order to make data transformation processes more reliable, scalable, and maintainable.\n\nIn this presentation, the Fanduel team will share the challenges faced during the migration, including overcoming the inertia of established data infrastructure, integrating with legacy systems, and ensuring a smooth transition without disrupting ongoing data operations. The migration process fostered newfound collaboration among data analysts, product owners, and engineers, with the added benefit of accommodating future business needs.&quot;}">Fanduel, America's leading sports betting company, migrated all of its data pipelines to integrate with dbt Cloud in order to make data transformation processes more reliable, scalable, and maintainable. In this presentation, the Fanduel team will share the challenges faced during the migration, including overcoming the inertia of established data infrastructure, integrating with legacy systems, and ensuring a smooth transition without disrupting ongoing data operations. The migration process fostered newfound collaboration among data analysts, product owners, and engineers, with the added benefit of accommodating future business needs.

Technical Program Manager

Senior Data Engineer

How Fanduel migrated a mature data organization

Fanduel, America's leading sports betting company, migrated all of its data pipelines to integrate with dbt Cloud in order to make data transformation processes more reliable, scalable, and...

Senior Analytics Engineer

Peer exchange: Strategies for skill acquisition

This session is specifically designed for beginners and will guide you step-by-step to building your very first data source from scratch. You'll learn to navigate our the new dbt Cloud IDE and gain a deep understanding of sources, source freshness, and tests. Discover the best practices of modern data management and set a strong foundation for your future projects.

This session is specifically designed for beginners and will guide you step-by-step to building your very first data source from scratch. You'll learn to navigate our the new dbt Cloud IDE and gain a deep understanding of sources, source freshness, and tests. Discover the best practices of modern data management and set a strong foundation for your future projects.

Fresh foundations: Migrate a stored procedure into your first dbt model

This session is specifically designed for beginners and will guide you step-by-step to building your very first data source from scratch. You'll learn to navigate our the new dbt Cloud IDE and gain a...

During the data team's short tenure (2.5 years) at ClickUp, they have built and scaled a fully modern data stack and implemented a warehouse-first data strategy. ClickUp's data is comprised of thousands of dbt models and upstream/downstream integrations with nearly every software at ClickUp. ClickUp use dbt Cloud and Snowflake to power dozens of downstream systems with audience creation, marketing optimization, predictive customer lifecycle ML, a PLG/PLS motion, and much more. This session will cover the foundational principles ClickUp follows and how warehouse-first thinking has unlocked tremendous value for ClickUp.

Head of Data

Warehouse-first data strategy at ClickUp

During the data team's short tenure (2.5 years) at ClickUp, they have built and scaled a fully modern data stack and implemented a warehouse-first data strategy. ClickUp's data is comprised of...

Highly technical Data teams can easily expound the virtues of Data Mesh to their technical peers, but getting leaders and lesser technical colleagues can be a challenge. In this talk, Pardhu Gunnam, CEO of Metaphor Data, will talk about the steps to sell the vision and build consensus across the organization for a successful implementation of Data Mesh.

Highly technical Data teams can easily expound the virtues of Data Mesh to their technical peers, but getting leaders and lesser technical colleagues can be a challenge. In this talk, Pardhu Gunnam, CEO of Metaphor Data, will talk about the steps to sell the vision and build consensus across the organization for a successful implementation of Data Mesh.

10 steps to successfully sell the vision of Data Mesh

Highly technical Data teams can easily expound the virtues of Data Mesh to their technical peers, but getting leaders and lesser technical colleagues can be a challenge. In this talk, Pardhu Gunnam,...

Two public servants at the City of Boston and the State of California are tasked with improving data services and data engineering practices within their respective governments. As part of the modernization process, they are adopting dbt and associated tools within their respective teams.

This session will discuss the similarities and differences between the implementations of dbt, and how some of the constraints and challenges of working in government shape both the technical and social design of data services. The speakers will reflect on successes, challenges, and lessons learned about adopting modern data tooling in state and local governments.

Two public servants at the City of Boston and the State of California are tasked with improving data services and data engineering practices within their respective governments. As part of the modernization process, they are adopting dbt and associated tools within their respective teams. This session will discuss the similarities and differences between the implementations of dbt, and how some of the constraints and challenges of working in government shape both the technical and social design of data services. The speakers will reflect on successes, challenges, and lessons learned about adopting modern data tooling in state and local governments.

Data Engineer

From coast to coast: Implementing dbt in the public sector

Two public servants at the City of Boston and the State of California are tasked with improving data services and data engineering practices within their respective governments. As part of the...

dbt Cloud makes it easy to safely contribute to data transformation. The always-accessible dbt Cloud IDE is now considerably faster, more helpful, and more reliable than ever before.

But that’s not all: dbt Cloud now natively supports the ability to develop via CLI, so everyone on your team can contribute from the environment they’re most comfortable.

Wherever you write code, join us live to see the future of dbt development in action.

dbt Cloud makes it easy to safely contribute to data transformation. The always-accessible dbt Cloud IDE is now considerably faster, more helpful, and more reliable than ever before. But that’s not all: dbt Cloud now natively supports the ability to develop via CLI, so everyone on your team can contribute from the environment they’re most comfortable. Wherever you write code, join us live to see the future of dbt development in action.

The new dbt Cloud development experience

dbt Cloud makes it easy to safely contribute to data transformation. The always-accessible dbt Cloud IDE is now considerably faster, more helpful, and more reliable than ever before.

But that’s not...

The Guild team migrated to Snowflake and dbt for their data warehousing needs and immediately saw the benefits of standardizing model structure, DRYer logic, data lineage and automated testing on Pull Requests.

But leveraging dbt didn’t solve everything. Pain points around maintaining model logic, handling historical data, and integrating data from over 60 source systems meant that analysts still struggled to provide a unified view of the business. The team knew that they needed to level up their processes and modeling again, and chose to adopt Data Vault (DV).

Brandon and Rebecca will take you behind the scenes of this decision to explain the benefits of Data Vault. They will highlight DV’s ability to handle complex data integration requirements while remaining agile and demonstrate that it complements other modern data concepts like domain-driven design and data mesh.

Attendees will learn what Data Vault is, when it can be a key component of a successful data strategy, and instances where it’s not the right fit. They will walk away with practical tips to successfully transition based on a real-world implementation.

Guild transformed their data warehouse; you can too!

The Guild team migrated to Snowflake and dbt for their data warehousing needs and immediately saw the benefits of standardizing model structure, DRYer logic, data lineage and automated testing on Pull Requests. But leveraging dbt didn’t solve everything. Pain points around maintaining model logic, handling historical data, and integrating data from over 60 source systems meant that analysts still struggled to provide a unified view of the business. The team knew that they needed to level up their processes and modeling again, and chose to adopt Data Vault (DV). Brandon and Rebecca will take you behind the scenes of this decision to explain the benefits of Data Vault. They will highlight DV’s ability to handle complex data integration requirements while remaining agile and demonstrate that it complements other modern data concepts like domain-driven design and data mesh. Attendees will learn what Data Vault is, when it can be a key component of a successful data strategy, and instances where it’s not the right fit. They will walk away with practical tips to successfully transition based on a real-world implementation. Guild transformed their data warehouse; you can too!

Senior Data Architect

60 sources and counting: Unlocking microservice integration with dbt and Data Vault

Community

The Guild team migrated to Snowflake and dbt for their data warehousing needs and immediately saw the benefits of standardizing model structure, DRYer logic, data lineage and automated testing on...

Dive into project auditing using the dbt_project_evaluator package, transforming your project's graph into a query-able table and catching misalignments. We'll cover key categories such as modeling, testing, documentation, structure, and performance, arming you with the knowledge to optimize your data pipelines.

Dive into project auditing using the dbt_project_evaluator package, transforming your project's graph into a query-able table and catching misalignments. We'll cover key categories such as modeling, testing, documentation, structure, and performance, arming you with the knowledge to optimize your data pipelines.

Zoom, audit, enhance: Using dbt project evaluator

Dive into project auditing using the dbt_project_evaluator package, transforming your project's graph into a query-able table and catching misalignments. We'll cover key categories such as modeling,...

dbt and Snowpark make a killer combination for machine learning deployments. In this workshop, learn how to create ML features and train ML models in Snowpark using dbt.

This workshop is for anyone who wants to get started with machine learning using dbt and Snowpark. It’s not necessary to have prior machine learning experience. Especially suited for those who are already using dbt for data modeling and want to extend the functionality to machine learning without much effort.

dbt and Snowpark make a killer combination for machine learning deployments. In this workshop, learn how to create ML features and train ML models in Snowpark using dbt.This workshop is for anyone who wants to get started with machine learning using dbt and Snowpark. It’s not necessary to have prior machine learning experience. Especially suited for those who are already using dbt for data modeling and want to extend the functionality to machine learning without much effort.

Director, Data Engineering 

Building end-to-end ML pipelines using dbt and Snowpark

dbt and Snowpark make a killer combination for machine learning deployments. In this workshop, learn how to create ML features and train ML models in Snowpark using dbt.

This workshop is for anyone...

Join us for a journey into the world of end-to-end analytics pipelines and how they can be a data engineer's best friend.

Learn how to automate boring tasks and create a safe haven for data engineers using the dynamic duo: dbt for transformative magic and GoodData for analytics awesomeness.

Combined with an data extraction and orchestration tools, you form the Voltron of easy to automate end-to-end analytics flows bringing data from source systems all the way through BI and to your end users.

And don't miss the grand finale where we reveal an alternative deployment on dbt Cloud that's so easy to orchestrate your coffee mug could do it. Prepare to laugh, learn, and level up your data game!

<span data-sheets-userformat='{"2":9021,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"11":4,"12":0,"16":11}' data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Join us for a journey into the world of end-to-end analytics pipelines and how they can be a data engineer's best friend.\n\nLearn how to automate boring tasks and create a safe haven for data engineers using the dynamic duo: dbt for transformative magic and GoodData for analytics awesomeness.\n\nCombined with an data extraction and orchestration tools, you form the Voltron of easy to automate end-to-end analytics flows bringing data from source systems all the way through BI and to your end users.\n\nAnd don't miss the grand finale where we reveal an alternative deployment on dbt Cloud that's so easy to orchestrate your coffee mug could do it. Prepare to laugh, learn, and level up your data game!&quot;}">Join us for a journey into the world of end-to-end analytics pipelines and how they can be a data engineer's best friend. Learn how to automate boring tasks and create a safe haven for data engineers using the dynamic duo: dbt for transformative magic and GoodData for analytics awesomeness. Combined with an data extraction and orchestration tools, you form the Voltron of easy to automate end-to-end analytics flows bringing data from source systems all the way through BI and to your end users. And don't miss the grand finale where we reveal an alternative deployment on dbt Cloud that's so easy to orchestrate your coffee mug could do it. Prepare to laugh, learn, and level up your data game!

VP of Product Strategy

Distinguished Software Engineer

Lazy devs unite! Building a data ecosystem that spoils data engineers

Join us for a journey into the world of end-to-end analytics pipelines and how they can be a data engineer's best friend.

Learn how to automate boring tasks and create a safe haven for data...

Traditional data warehouses excel at churning through terabytes of data for historical analysis. But for real-time, business-critical use cases, traditional data warehouses can’t produce results fast enough—and they still rack up a huge bill in the process.
 
So when Ramp’s data engineering team needed to serve complex analytics queries on the critical path of their production application, they knew they needed a new tool for the job. Enter Materialize, the first operational data warehouse. Like a traditional data warehouse, Materialize centralizes the data from all of a business’s production systems, from application databases to SaaS tools. But unlike a traditional data warehouse, Materialize enables taking immediate and automatic action when that data changes. Queries that once took hours or minutes to run are up-to-date in Materialize within seconds.


In this talk, we’ll present how Ramp is unlocking new real-time use cases using Materialize as their operational data warehouse. The best part? The team still uses dbt for data modeling and deployment management, just like they are able to with their traditional batch workloads.

<span data-sheets-userformat='{"2":15293,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"10":2,"11":4,"12":0,"14":{"1":2,"2":0},"15":"Calibri","16":11}' data-sheets-value='{"1":2,"2":"Traditional data warehouses excel at churning through terabytes of data for historical analysis. But for real-time, business-critical use cases, traditional data warehouses can’t produce results fast enough—and they still rack up a huge bill in the process.\n \n So when Ramp’s data engineering team needed to serve complex analytics queries on the critical path of their production application, they knew they needed a new tool for the job. Enter Materialize, the first operational data warehouse. Like a traditional data warehouse, Materialize centralizes the data from all of a business’s production systems, from application databases to SaaS tools. But unlike a traditional data warehouse, Materialize enables taking immediate and automatic action when that data changes. Queries that once took hours or minutes to run are up-to-date in Materialize within seconds.\n\n In this talk, we’ll present how Ramp is unlocking new real-time use cases using Materialize as their operational data warehouse. The best part? The team still uses dbt for data modeling and deployment management, just like they are able to with their traditional batch workloads."}'>Traditional data warehouses excel at churning through terabytes of data for historical analysis. But for real-time, business-critical use cases, traditional data warehouses can’t produce results fast enough—and they still rack up a huge bill in the process. So when Ramp’s data engineering team needed to serve complex analytics queries on the critical path of their production application, they knew they needed a new tool for the job. Enter Materialize, the first operational data warehouse. Like a traditional data warehouse, Materialize centralizes the data from all of a business’s production systems, from application databases to SaaS tools. But unlike a traditional data warehouse, Materialize enables taking immediate and automatic action when that data changes. Queries that once took hours or minutes to run are up-to-date in Materialize within seconds. <span data-sheets-userformat='{"2":15293,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"10":2,"11":4,"12":0,"14":{"1":2,"2":0},"15":"Calibri","16":11}' data-sheets-value='{"1":2,"2":"Traditional data warehouses excel at churning through terabytes of data for historical analysis. But for real-time, business-critical use cases, traditional data warehouses can’t produce results fast enough—and they still rack up a huge bill in the process.\n \n So when Ramp’s data engineering team needed to serve complex analytics queries on the critical path of their production application, they knew they needed a new tool for the job. Enter Materialize, the first operational data warehouse. Like a traditional data warehouse, Materialize centralizes the data from all of a business’s production systems, from application databases to SaaS tools. But unlike a traditional data warehouse, Materialize enables taking immediate and automatic action when that data changes. Queries that once took hours or minutes to run are up-to-date in Materialize within seconds.\n\n In this talk, we’ll present how Ramp is unlocking new real-time use cases using Materialize as their operational data warehouse. The best part? The team still uses dbt for data modeling and deployment management, just like they are able to with their traditional batch workloads."}'>In this talk, we’ll present how Ramp is unlocking new real-time use cases using Materialize as their operational data warehouse. The best part? The team still uses dbt for data modeling and deployment management, just like they are able to with their traditional batch workloads.

Staff Software Engineer, Data Platform

Operationalizing Ramp’s data with dbt and Materialize

Traditional data warehouses excel at churning through terabytes of data for historical analysis. But for real-time, business-critical use cases, traditional data warehouses can’t produce results fast...

This session will address the problem of how to leverage dbt Cloud to support domain user development as we migrate from a centralized analytics environment towards a distributed Data Mesh analytics environment.

Data Architect

Leveraging dbt Cloud for a distributed domain-driven development environment

This session will address the problem of how to leverage dbt Cloud to support domain user development as we migrate from a centralized analytics environment towards a distributed Data Mesh analytics...

This session will share Rivian’s journey building an analytics ecosystem from scratch over the last two years, centered around dbt and dbt Cloud. Through this work, Rivian is driving towards a healthy data mesh that links data and developers across many domains at the company, to enable rapid growth and value as they ramp to produce their new fleet of EVs and keep the world adventurous.

<span data-sheets-userformat='{"2":13249,"3":{"1":0},"9":0,"10":1,"11":4,"12":0,"15":"Calibri","16":11}' data-sheets-value='{"1":2,"2":"This session will share Rivian’s journey building an analytics ecosystem from scratch over the last two years, centered around dbt and dbt Cloud. Through this work, Rivian is driving towards a healthy data mesh that links data and developers across many domains at the company, to enable rapid growth and value as they ramp to produce their new fleet of EVs and keep the world adventurous."}'>This session will share Rivian’s journey building an analytics ecosystem from scratch over the last two years, centered around dbt and dbt Cloud. Through this work, Rivian is driving towards a healthy data mesh that links data and developers across many domains at the company, to enable rapid growth and value as they ramp to produce their new fleet of EVs and keep the world adventurous.

Manager, Data Science Analytics

Driving toward data mesh at Rivian

This session will share Rivian’s journey building an analytics ecosystem from scratch over the last two years, centered around dbt and dbt Cloud. Through this work, Rivian is driving towards a...

As analytics engineers, we know that getting the data model right is crucial for successful data analytics. However, communicating requirements and needs can be a challenge, especially for junior data team members. After noticing too many post-PR revisions and missed requirements, we at Wellthy began developing a communication framework that has helped us streamline and clarify the requirements gathering process for new models. 

Our framework is designed to help analysts visualize and anticipate what they need from a dataset, resulting in a more efficient and effective data modeling process for the analytics engineer. In this talk, we will share our experiences and best practices using these frameworks, as well as the benefits we have seen in terms of accuracy, efficiency, and time savings.

As analytics engineers, we know that getting the data model right is crucial for successful data analytics. However, communicating requirements and needs can be a challenge, especially for junior data team members. After noticing too many post-PR revisions and missed requirements, we at Wellthy began developing a communication framework that has helped us streamline and clarify the requirements gathering process for new models. Our framework is designed to help analysts visualize and anticipate what they need from a dataset, resulting in a more efficient and effective data modeling process for the analytics engineer. In this talk, we will share our experiences and best practices using these frameworks, as well as the benefits we have seen in terms of accuracy, efficiency, and time savings.

Getting the data model right the FIRST time

As analytics engineers, we know that getting the data model right is crucial for successful data analytics. However, communicating requirements and needs can be a challenge, especially for junior...

CI (Continuous Integration) in dbt Cloud makes it easy to test every change you make prior to deploying. It’s a hallmark of mature analytics workflows.

We’ve made some major improvements to dbt Cloud CI, so it’s easier than ever to prevent breaking changes, save on costs, and keep those pesky stakeholders happy.

Join the dbt Labs product team on this magical journey to a world of better data quality, and see for yourself what CI can do for you.

CI (Continuous Integration) in dbt Cloud makes it easy to test every change you make prior to deploying. It’s a hallmark of mature analytics workflows. We’ve made some major improvements to dbt Cloud CI, so it’s easier than ever to prevent breaking changes, save on costs, and keep those pesky stakeholders happy. Join the dbt Labs product team on this magical journey to a world of better data quality, and see for yourself what CI can do for you.

Better CI for better data quality

CI (Continuous Integration) in dbt Cloud makes it easy to test every change you make prior to deploying. It’s a hallmark of mature analytics workflows.

We’ve made some major improvements to dbt...

Unwind and connect with like-minded data experts in a vibrant and energizing atmosphere on the yacht after Coalesce. Don't worry, we won't be setting sail navigating the digital seas. — the yacht will stay docked for the evening for us to enjoy the beautiful San Diego sunset.

This event is hosted by Dremio, Airbyte, and Rudderstack. 

Link to register —  https://dataondeckdbt.splashthat.com/

Unwind and connect with like-minded data experts in a vibrant and energizing atmosphere on the yacht after Coalesce. Don't worry, we won't be setting sail navigating the digital seas. — the yacht will stay docked for the evening for us to enjoy the beautiful San Diego sunset.This event is hosted by Dremio, Airbyte, and Rudderstack.&nbsp;Link to register —&nbsp;&nbsp;<a href="https://dataondeckdbt.splashthat.com/">https://dataondeckdbt.splashthat.com/</a>

Data on deck | Yacht cocktail party | Ahoy, data aficionados, and tech trailblazers! 

Unwind and connect with like-minded data experts in a vibrant and energizing atmosphere on the yacht after Coalesce. Don't worry, we won't be setting sail navigating the digital seas. — the yacht...

October 17th, 2023 | 8:00-11:00PM PT | Punch Bowl Social San Diego |




RSVP here: http://events.montecarlodata.com/datasocial

October 17th, 2023 | 8:00-11:00PM PT | Punch Bowl Social San Diego | RSVP here: http://events.montecarlodata.com/datasocial

Data social late night party 

Class: "Unwind" Slow Flow

Participants will be guided through a grounded breath work and a gentle seated and standing yoga sequence to allow the body to relax. This is a great option for those who are new to yoga.

Mats will be provided.

Class: "Unwind" Slow FlowParticipants will be guided through a&nbsp;grounded breath work and a gentle seated&nbsp;and standing yoga sequence to allow the&nbsp;body to relax.&nbsp;This is a great option for those&nbsp;who are new to yoga.Mats will be provided.

"Unwind" slow flow — brought to you by Census

Class: "Unwind" Slow Flow

Participants will be guided through a grounded breath work and a gentle seated and standing yoga sequence to allow the body to relax. This is a great option for those who...

Join us for a cozy morning at Coalesce's animated breakfast session, "How is AI disrupting Data Governance.", animated by Tristan Mayer, CEO & Co-founder of CastorDoc. Here's a sneak peek of what you can look forward to:- Discover how AI is revolutionizing data governance, with practical insights and relatable examples.
- Learn actionable tips to infuse AI into your data governance practices, boosting data quality, fortifying security, and staying compliant.
- Explore the partnership between humans and AI, ensuring trustworthy data and thriving organizations. Connect with like-minded professionals and explore exciting AI possibilities for data governance.
See you there!

Join us for a cozy morning at Coalesce's animated breakfast session, "How is AI disrupting Data Governance.", animated by Tristan Mayer, CEO &amp; Co-founder of CastorDoc. Here's a sneak peek of what you can look forward to:- Discover how AI is revolutionizing data governance, with practical insights and relatable examples. - Learn actionable tips to infuse AI into your data governance practices, boosting data quality, fortifying security, and staying compliant. - Explore the partnership between humans and AI, ensuring trustworthy data and thriving organizations. Connect with like-minded professionals and explore exciting AI possibilities for data governance. See you there!

Breakfast breakout: How is AI disrupting data governance

Join us for a cozy morning at Coalesce's animated breakfast session, "How is AI disrupting Data Governance.", animated by Tristan Mayer, CEO & Co-founder of CastorDoc. Here's a sneak peek of what you...

At dbt Labs, we believe in putting our money where our mouth is, so much so that we use our own product to optimize our business. How do we do it? Join dbt Labs’ CFO, Daniel Le, for a conversation about how data leaders can align with their executive team and use dbt Cloud to deliver metrics, analysis, and data products that keep the business executing efficiently.

At dbt Labs, we believe in putting our money where our mouth is, so much so that we use our own product to optimize our business. How do we do it? Join dbt Labs’ CFO, Daniel Le, for a conversation about how data leaders can align with their executive team and use dbt Cloud to deliver metrics, analysis, and data products that keep the business executing efficiently.

Chief Financial Officer

Sr. Data Engineer

Analytics Lead

dbt Labs on dbt: An executive perspective

At dbt Labs, we believe in putting our money where our mouth is, so much so that we use our own product to optimize our business. How do we do it? Join dbt Labs’ CFO, Daniel Le, for a conversation...

This course will focus on a community favorite - incremental models. Incremental models are a key lever in dbt for accelerating development of incremental builds and reducing warehouse spend. We will start with the basics of incremental models and then get into the nuances of how to use them in different situations.

- After this course you will be able to:

   - Identify ideal use cases for incremental models

   - Configure an incremental model

   - Navigate the nuances of common challenges that arise with incremental loads

   - Demonstrate cost savings when converting a model to an incremental materialization

- Prerequisites: The prerequisites for this course include:

   - dbt Fundamentals, specifically data models and table/view materializations.

- What to bring: You will need to bring your own laptop to complete the hands-on exercises. We will provide all the other sandbox environments for dbt Cloud and data platform.

This course will focus on a community favorite - incremental models. Incremental models are a key lever in dbt for accelerating development of incremental builds and reducing warehouse spend. We will start with the basics of incremental models and then get into the nuances of how to use them in different situations.- After this course you will be able to:&nbsp; &nbsp;- Identify ideal use cases for incremental models&nbsp; &nbsp;- Configure an incremental model&nbsp; &nbsp;- Navigate the nuances of common challenges that arise with incremental loads&nbsp; &nbsp;- Demonstrate cost savings when converting a model to an incremental materialization- Prerequisites: The prerequisites for this course include:&nbsp; &nbsp;- dbt Fundamentals, specifically data models and table/view materializations.- What to bring: You will need to bring your own laptop to complete the hands-on exercises. We will provide all the other sandbox environments for dbt Cloud and data platform.

Training: Cost optimization with incremental models

This course will focus on a community favorite - incremental models. Incremental models are a key lever in dbt for accelerating development of incremental builds and reducing warehouse spend. We will...

Get certified at Coalesce! Choose from one of two certifications: dbt Developer and dbt Cloud Administrator.  Your purchase includes sitting for one attempt for one of the two exams in person at Coalesce. Both exams are 2 hours in length, proctored in person, and you will need to bring your own computer to take the exam.

Get certified at Coalesce! Choose from one of two certifications: dbt Developer and dbt Cloud Administrator. &nbsp;Your purchase includes sitting for one attempt for one of the two exams in person at Coalesce. Both exams are 2 hours in length, proctored in person, and you will need to bring your own computer to take the exam.

Get certified at Coalesce! Choose from one of two certifications: dbt Developer and dbt Cloud Administrator.  Your purchase includes sitting for one attempt for one of the two exams in person at...

Take a look at how the finance data team at Snowflake leverages dbt to drive strategic decision-making within the finance and accounting organizations. This talk spans topics including development velocity, cost governance, and data stewardship.

Need for speed (and less spending): The story of finance data at Snowflake

Take a look at how the finance data team at Snowflake leverages dbt to drive strategic decision-making within the finance and accounting organizations. This talk spans topics including development...

San Diego Agenda

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