Coalesce Online

Welcome remarks

User stitching can be cumbersome; third-party tracking, expiring cookies, browser privacy features, and users not being logged in. These challenges require companies to rethink how they collect and use data, and how they want to establish a mutually beneficial relationship with their users. In this session, learn how to solve these problems with an architecture that allows you to implement and control first-party tracking with data creation and data modelling, enabling you to easily set up user stitching. Learn how this directly drives the development of smarter data products that leverage user identity resolution, leading to more user engagement and growth. 

User stitching can be cumbersome; third-party tracking, expiring cookies, browser privacy features, and users not being logged in. These challenges require companies to rethink how they collect and use data, and how they want to establish a mutually beneficial relationship with their users. In this session, learn how to solve these problems with an architecture that allows you to implement and control first-party tracking with data creation and data modelling, enabling you to easily set up user stitching. Learn how this directly drives the development of smarter data products that leverage user identity resolution, leading to more user engagement and growth.

Emiel is the Lead Analytics Engineer at Snowplow. Emiel joined Snowplow in 2021 to expand the data modelling capabilities of Snowplow users and is currently mainly focused on building out new data models for expanded use cases to help accelerate the time to insight for Snowplow users. Prior to working at Snowplow, Emiel was a one-man data team at a Fintech startup, as well as being a Data/Product Analyst at a rapidly growing scale-up.

Staff Analytics Engineer

Build scalable data products leveraging user stitching

Online Ecosystem Sponsor

The off-Broadway, on-Bourbon adaption of "Data—The Musical." Featuring an all star cast you probably don't know outside that one humerous Twitter exchange, caroling a cathartic chorus of communal data dolors. The internal tracking tag our events team added to this session was, "Rad experiential social content" and I, the writer of this session summary, thought that said, "Rad experimental social construct" and I for one would like to see which ends up being true. Join me.

The off-Broadway, on-Bourbon adaption of &quot;Data—The Musical.&quot; Featuring an all star cast you probably don't know outside that one humerous Twitter exchange, caroling a cathartic chorus of communal data dolors. The internal tracking tag our events team added to this session was, &quot;Rad experiential social content&quot; and I, the writer of this session summary, thought that said, &quot;Rad experimental social construct&quot; and I for one would like to see which ends up being true. Join me.

Tiankai loves to combine his analytical mindset with creative thinking, and has been using his musical talents to make data more fun in all his career so far.

He worked for almost 10 years in Digital Marketing Analytics and is now leading the Product Data Governance team at adidas. A focus on people is important to him, and he is an ambassador for a more empathetic collaboration when it comes to data.

Senior Director, Product Data Governance

Data - The Musical

Emerging Voices

Community Vibes

Join us in Storyville Hall for the most important meal of the day. And your tea 🫖 and coffee ☕️.

Coalesce New Orleans

Breakfast

As analytics engineers, we make impact by building analytics things (models, pipelines, visualizations) that help stakeholders make decisions about what to do next. What if we could also make impact by driving a culture of experimentation—which will help those same stakeholders make decisions too? 

Join Adam Stone (Netlify) as he draws on his vast experimentation experience and explains how analytics engineer can use a combination of a program-building mindset, organizational mentoring (and cheerleading), and off-the-shelf tools to partner with product and engineering teams to quickly spin up meaningful experimentation. 

As analytics engineers, we make impact by building analytics things (models, pipelines, visualizations) that help stakeholders make decisions about what to do next. What if we could also make impact by driving a culture of experimentation—which will help those same stakeholders make decisions too?&nbsp;Join Adam Stone (Netlify) as he draws on his vast experimentation experience and explains how analytics engineer can use a combination of a program-building mindset, organizational mentoring (and cheerleading), and off-the-shelf tools to partner with product and engineering teams to quickly spin up meaningful experimentation.

I've been an analytics engineer & data scientist for more than five years now, for tech companies small and big. Before this, I was a kindergarten teacher and a cognitive neuroscientist (not at the same time, though). Originally from San Diego, I now roost in London as a Senior Analytics Engineer at Netlify.

Sr. Analytics Engineer

What does analytics engineering have to do with product experimentation?

Mindset Shift

Blurred Boundaries - Engineering

Leading Voices

We'll explain how Spectacles makes dbt developers more confident and efficient by revealing the impact of their proposed changes on Looker—preventing dashboard outages and angry analysts. 

We'll explain how Spectacles makes dbt developers more confident and efficient by revealing the impact of their proposed changes on Looker—preventing dashboard outages and angry analysts.

Josh Temple is the co-founder and CTO of Spectacles, a continuous integration and DevOps solution for Looker developers. Previously, Josh worked a Product Manager at Spotify, developing core datasets for data scientists and MLEs. As a one-person data team for the iconic NYC bakery Milk Bar, Josh established a business intelligence platform to power their transformation into an omnichannel retailer. Josh is an avid open source developer, and created the lkml LookML parser, which enables custom Looker tooling across the industry. He is an advocate for testing and automation in analytics and data engineering and is old enough to remember when the Modern Data Stack was modern.

Founder & CTO

Prevent dbt Changes from Breaking Looker with Spectacles

In this talk, Marielle Dado (Paddle) will introduce the concept of knowledge construction (a theory that featured heavily in their previous life as a learning psychologist/cognitive scientist), why they think analytics engineers should see themselves as "knowledge creators," and how this framing can lead to less ad-hoc querying and more robust data models.



In this talk, Marielle Dado (Paddle) will introduce the concept of knowledge construction (a theory that featured heavily in their previous life as a learning psychologist/cognitive scientist), why they think analytics engineers should see themselves as "knowledge creators," and how this framing can lead to less ad-hoc querying and more robust data models.

I'm a data engineer based in Berlin, Germany. In a past life, I was a learning sciences researcher (PhD), which means I combined theories from psychology and methods from computer science to understand how people learn.

I also really love cats, good food and specialty coffee.

Staff Data Engineer

I don’t build models, I construct knowledge

Navigate Your Career

Data teams aren't only working in Excel and Tableau anymore. We're working in GitHub, VSCode, BigQuery, and our command line. We have the modern data pipeline and we're developing a lot more like engineers...but our development workflows are anything but modern. Why don't we get developer previews? Or the ability to test our changes to anything downstream of dbt? Why do our tools not talk to each other? We believe that analytics engineers deserve better and we want to show you what "better data development" could look like in the modern data pipeline (we promise, it's really nice). 

Data teams aren't only working in Excel and Tableau anymore. We're working in GitHub, VSCode, BigQuery, and our command line. We have the modern data pipeline and we're developing a lot more like engineers...but our development workflows are anything but modern. Why don't we get developer previews? Or the ability to test our changes to anything downstream of dbt? Why do our tools not talk to each other? We believe that analytics engineers deserve better and we want to show you what "better data development" could look like in the modern data pipeline (we promise, it's really nice).

Katie loves a good chart. She's a data scientist at heart, and leads product at Lightdash.

Head of Product and Data

Ian loves difficult problems and even moreso, loves making them easier. He’s worked across corporates in Oil and Gas and Investment Banking but found his niche working in a broad range of roles at high-growth startups. Whether it be technical sales, running user research experiments, building data scrapers, forming and maintaining strategic partnerships or running tie dye workshops at data conferences, he loves the variety and novelty just like a kid in a candy shop.

Head of Growth & Community

From Excel to IDE and beyond: The origins and future of the data developer

Striim is a unified data streaming and integration product that offers change capture (CDC) enabling continuous replication from popular databases to target data warehouses. John will show how you can use Striim and dbt to stream and model data to deliver fresh insights to all your business consumers. 

Striim is a unified data streaming and integration product that offers change capture (CDC) enabling continuous replication from popular databases to target data warehouses. John will show how you can use Striim and dbt to stream and model data to deliver fresh insights to all your business consumers.

John has been with Striim for nine years. He studied computer science with a special focus on distributed systems and data visualization in college. John started at Striim as a software engineer, contributing to both front and back end features before moving to product management in 2016. He now leads the PLG team at Striim and hosts the, “What’s New in Data” podcast.

Director of Product 

Deliver Fresh Data with dbt and Striim

A Senior Business Analyst, an Analytics Engineer, and a Data Quality Engineer walk into the office. All three of them ask for dbt. Why? Join Coco Haury (WEX, Inc.) to hear about the universal appeal of dbt to folks outside traditional data engineer and analyst roles, and how dbt has helped WEX modernize their entire enterprise data platform.

Coco is a high key nerd with jock tendencies who drinks too much coffee and loves animals. When she's not trying to change the world through the power of positive team collaboration, high quality testing, and efficient development processes, you can find Coco roller skating on the Maine coast.

Senior Data Quality Engineer

Alicia had been in the data analytics space for just over a decade with roles across risk, marketing and finance working to eloquently bridge the gap between business and technology. Through that times she has become an advocate for testing, automation and remediating manual processes wherever possible.  She finds joy in educating and empowering new data users.

Lead Data Analyst

Rizwaan is working as a data engineer building modern data pipelines and solutions to day-to-day problems at an enterprise level. He's most interested in SQL, python, and the intersection of art and code. When Rizwaan's not working, you can find him watching soccer, playing video games and listening to podcasts.

Data Engineer 3

Finding common ground, to build common ground: dbt at the enterprise

Blurred Boundaries - Operations

Leading Teams

Strategies for Scale

Format: Online

Location: Coalesce Online

Duration: 90 minutes

Audience: Analytics Engineer, Data Analyst, Data Engineer, Business Stakeholder

Experience level: Beginner, New to dbt

Bring your own laptop: Yes

Description:

•This fast paced course will focus on rapidly teaching you the basics of dbt. We’ll cover the foundational knowledge you’ll need to experience the power of dbt. You will walk away understanding how dbt enables rapid development of transformation code, quality assurance, and deploying your transformations on a schedule.
You’ll learn how to:

•Transform data using dbt
•Provide clarity around your project through testing and documentation
•Incorporate dbt best-practices in your project (CTEs, references, etc.)
Prerequisites:

•Basic familiarity with SQL
•No experience with data warehouses or dbt required
Note: This session is a free event that is only available online. We will be in touch to request additional information to get you registered for the event.

Format: OnlineLocation: Coalesce OnlineDuration: 90 minutesAudience: Analytics Engineer, Data Analyst, Data Engineer, Business StakeholderExperience level: Beginner, New to dbtBring your own laptop: YesDescription:<ul><li>This fast paced course will focus on rapidly teaching you the basics of dbt. We’ll cover the foundational knowledge you’ll need to experience the power of dbt. You will walk away understanding how dbt enables rapid development of transformation code, quality assurance, and deploying your transformations on a schedule.</li></ul>You’ll learn how to:<ul><li>Transform data using dbt</li><li>Provide clarity around your project through testing and documentation</li><li>Incorporate dbt best-practices in your project (CTEs, references, etc.)</li></ul>Prerequisites:<ul><li>Basic familiarity with SQL</li><li>No experience with data warehouses or dbt required</li></ul>Note:&nbsp;This session is a free event that is only available online. We will be in touch to request additional information to get you registered for the event.

Michelle got her data science background conducting astrophysics data analysis research. Now, she uses those skills as an instructional designer at dbt Labs, where she collaborates with the rest of the customer success team to create meaningful learning experiences.

Instructional Designer

Jeremy learned the importance of data quality working for an education non-profit advocating for high speed and affordable Internet in public schools. He recently joined dbt Labs to help share the data skills he’s learned and empower other practitioners to make more informed decisions with data.

Sr. Technical Instructor

Zero to dbt

Training

Fivetran and dbt Cloud continue to gain traction and popularity among some of the most innovative companies, and for good reason. When combined, Fivetran and dbt Cloud provide an automated, performant, end-to-end ELT pipeline. Join our session where Nikolay Voronchikhin will walk us through Cisco’s journey to building a successful data stack with Fivetran and dbt Cloud. Discover best practices to maximize this powerful combination in your business. 

Fivetran and dbt Cloud continue to gain traction and popularity among some of the most innovative companies, and for good reason. When combined, Fivetran and dbt Cloud provide an automated, performant, end-to-end ELT pipeline. Join our session where Nikolay Voronchikhin will walk us through Cisco’s journey to building a successful data stack with Fivetran and dbt Cloud. Discover best practices to maximize this powerful combination in your business.

Nikolay Voronchikhin is a Cloud Solutions & Applications Analyst at Cisco. He is currently working on the Data Platform team in the Chief Data & Analytics Office, where he is an SME and owner of the Fivetran Platform. In his free time, Nikolay enjoys attending theme parks and sporting events, planning and traveling to new places, and understanding how things work.

Cloud Solutions & Applications Analyst

Alex manages the product marketing for Fivetran's data transformation features, including our partnership with dbt Labs. He has spent the past several years talking to clients and prospects about the power of data.

Senior Product Marketing Manager, Transformations

How Cisco powers its modern ELT approach with Fivetran and dbt Labs

Do you enjoy waking up in the morning and playing the daily Wordle puzzle? Have you been wishing there was a similar game for you to play, but was built specifically for data folks? Well, you are in luck! 

Join Joe Markiewicz (analytics engineer by day, video game maker by night) as he explains his inspiration and how he leveraged dbt and BigQuery to create a new video game aimed at helping experienced analysts keep their SQL skills sharp and data newcomers increase their SQL literacy.

Do you enjoy waking up in the morning and playing the daily Wordle puzzle? Have you been wishing there was a similar game for you to play, but was built specifically for data folks? Well, you are in luck!&nbsp;Join Joe Markiewicz (analytics engineer by day, video game maker by night) as he explains his inspiration and how he leveraged dbt and BigQuery to create a new video game aimed at helping experienced analysts keep their SQL skills sharp and data newcomers increase their SQL literacy.

Video Game Developer

SQL: The video game

Technical Impact

I have spoken with many data people about their most hated tool: the “service desk” used to manage the requests of others. 

One team has a Slack channel called “data-sucks.” The name tells you much about people’s feelings. Others use Google forms, JIRA Service Desk, or some horrifying combination. 

Teams debate the ways that they can prevent, or reduce, these requests in the first place. Should we make requests more difficult? Are we stuck in the service trap? Should we enable self-service analytics? 

Initiatives are undertaken, and afterwards is a terrible truth: the questions and our loathing remain. And the others? They still hate making the requests, too. 

Why do we all feel this way, and why are we still unable to do anything about it?

I have spoken with many data people about their most hated tool: the “service desk” used to manage the requests of others.&nbsp;One team has a Slack channel called “data-sucks.” The name tells you much about people’s feelings. Others use Google forms, JIRA Service Desk, or some horrifying combination.&nbsp;Teams debate the ways that they can prevent, or reduce, these requests in the first place. Should we make requests more difficult? Are we stuck in the service trap? Should we enable self-service analytics?&nbsp;Initiatives are undertaken, and afterwards is a terrible truth: the questions and our loathing remain. And the others? They still hate making the requests, too.&nbsp;Why do we all feel this way, and why are we still unable to do anything about it?

Nick Freund is a data driven leader with a decade of experience leading startups focused on product-led growth. As the Founder of Workstream.io, Nick spearheads a seed-stage technology startup that helps data teams manage critical analytics assets and workflows. 
 
 Prior to Workstream.io, Nick earned his MBA at Harvard and served as VP of Operations for BetterCloud, an independent software vendor that offers the leading SaaS Operations Management solution. He started his career as an early employee at Tesla, where he led finance and analytics for the Tesla Roadster and Model S programs.

Founder & CEO

Data teams: kill your service desk

Take a break and grab lunch with us in Storyville Hall 🍽.

Take a break and grab lunch with us in Storyville Hall 🍽.

Lunch

Growing demand for data puts Analytics Engineers under pressure to move quickly, without introducing bugs to their models. If you find yourself pushing dbt’s testing framework to the limit, or waiting for careful code reviews before deploying to production, this talk is for you. Join Egor Gryaznov, CTO of Bigeye and former Staff Data Engineer at Uber, to learn how to take your workflow to the next level with blue-green deployments.  This talk will cover:  -CI/CD pipeline best practices for Analytics Engineers -Automatically validating pipeline changes with blue-green deployments -Tracking data changes through merge requests for visibility and reliability More details coming soon!

Growing demand for data puts Analytics Engineers under pressure to move quickly, without introducing bugs to their models. If you find yourself pushing dbt’s testing framework to the limit, or waiting for careful code reviews before deploying to production, this talk is for you. Join Egor Gryaznov, CTO of Bigeye and former Staff Data Engineer at Uber, to learn how to take your workflow to the next level with blue-green deployments. &nbsp;This talk will cover: &nbsp;-CI/CD pipeline best practices for Analytics Engineers -Automatically validating pipeline changes with blue-green deployments -Tracking data changes through merge requests for visibility and reliability More details coming soon!

Egor is co-founder and Chief Technology Officer at Bigeye. Before starting Bigeye, Egor was a staff engineer at Uber where he scaled the company’s first data warehouse, supporting thousands of internal users and mission-critical workloads.

Co-Founder & CTO

Move Fast and (Don’t) Break Things: Testing dbt Models with Bigeye

The tech talent shortage is now just the tech talent situation—it’s here to stay. But when growth is glamorous, throwing bodies at a problem is still winning out over doing more with less. Until this year. The current economic climate is showing us that it’s time to make “efficient” the new “sexy.” In this talk, Matthieu DiMercurio (Dialogue.co) shares how to cultivate executive buy-in to unite Data, Engineering, and IT in a joint endeavor to automate tasks, build smarter systems, and cut costly silos.

Matthieu is VP, Tech Operations at Dialogue Health Technologies, the Canadian leader in virtual care.
In his role, he leads the data platform, analytics, IT, Security and support teams.
Before joining Dialogue, Matthieu worked as a data scientist/engineer/analyst/[insert data-anything role here] at Intel Security and Ubisoft.
Matthieu is the cofounder and organizer of MTL DATA, the largest data community in Montreal.
He is also a subject matter expert at MIT Horizon, supporting the development of a training program in Big Data.

VP, Tech-ops

Efficient is the new sexy — A minimalist approach to growth

M&A can be fast-paced and messy, but companies often count on the data team to provide stability in metrics and data consolidation. After Dutchie acquired two companies in March 2021, the data team needed to integrate datasets from those companies into existing data platforms, and has some takeaways that may be helpful towards data practitioners one day finding themselves in a similar situation.

M&amp;A can be fast-paced and messy, but companies often count on the data team to provide stability in metrics and data consolidation. After Dutchie acquired two companies in March 2021, the data team needed to integrate datasets from those companies into existing data platforms, and has some takeaways that may be helpful towards data practitioners one day finding themselves in a similar situation.

Jacob Adler is a Sr. Analytics Engineer at Future. Prior to that, he was the Analytics Engineering Lead at Dutchie, where he was the first data hire and built out the data function using dbt, Snowflake, Fivetran, and Looker. He previously worked in CRM and marketing analytics at Gopuff and in data science at Caesars Entertainment, and graduated from the Wharton School of the University of Pennsylvania with a BSE concentrating in Statistics and Operations, Information, and Decisions.

Sr. Analytics Engineer 

Cameron Lichtenstein is a Senior Analytics Engineer at Dutchie and has spent the last decade working across data, product, and operations. Before Dutchie, he served as an analyst for product and go-to-market functions at Envoy and Intercom through billion-dollar valuations using dbt, Redshift, Looker, and Tableau.

Senior Analytics Engineer

Navigating data consolidation after M&A

Everyone is talking about data lineage these days, and for a good reason. Data lineage helps ensure better data quality across your modern data stack. But not everyone speaks the same lineage language. Data engineers use lineage for impact and root cause analysis. Analysts and Analytics engineers use lineage to trace jobs and transformations in their warehouses. And consumers use lineage to understand why data never reached their expected destination. This results in a narrow, siloed view lineage in which only one group benefits. It’s time to stop using siloed lineage views for pretty graphs and start using end-to-end lineage to drive focused actions. In the talk, you will learn: 

• How data quality tailors to specific needs of data engineers, analysts, & consumers 

• How data lineage should drive actions 

• A real-world example of end-to-end data lineage with Airflow, dbt, Spark, and Redshift 

Everyone is talking about data lineage these days, and for a good reason. Data lineage helps ensure better data quality across your modern data stack. But not everyone speaks the same lineage language. Data engineers use lineage for impact and root cause analysis. Analysts and Analytics engineers use lineage to trace jobs and transformations in their warehouses. And consumers use lineage to understand why data never reached their expected destination. This results in a narrow, siloed view lineage in which only one group benefits. It’s time to stop using siloed lineage views for pretty graphs and start using end-to-end lineage to drive focused actions. In the talk, you will learn:&nbsp;• How data quality tailors to specific needs of data engineers, analysts, &amp; consumers&nbsp;• How data lineage should drive actions&nbsp;• A real-world example of end-to-end data lineage with Airflow, dbt, Spark, and Redshift

Shani Keynan is VP of Product Management at Databand. An entrepreneur at heart, Shani is driven by the pursuit of creating true value. He's developed products in a variety of verticals from Web3.0 at Vidarmo to automotive connected vehicle platform Otonomo. At Databand.ai, he's leading the product team to design a proactive data observability platform that makes data reliable and usable across the organization.

VP of Product Management

Beyond pretty graphs: How end-to-end lineage drives better actions

Format: In-person

Location: Coalesce New Orleans

Duration: 4 hours

Audience: Analytics Engineer, Data Analyst, Data Engineer, Business Stakeholder

Experience level: Beginner, New to dbt

Bring your own laptop: Yes

Description:

•In this course, we are teaching dbt Fundamentals in person! We will focus on covering the foundation knowledge to get started with dbt and set you on your way to building your very own dbt project.  We will cover models, sources, tests, docs, and deployment.  You will be provided access to a dbt Cloud account and data warehouse for this training.
You’ll learn how to:

•Build a dbt project in dbt Cloud
•Provide clarity around your project through documentation
•Incorporate dbt best-practices in your project (CTEs, references, etc.)
Prerequisites:

•Basic familiarity with SQL
•No programming experience required
•No experience with data warehouses or dbt required
Note: This session requires a premium ticket to attend. If you would like to upgrade to a premium ticket, please contact our team at coalesce@getdbt.com.

Format: In-personLocation: Coalesce New OrleansDuration: 4 hoursAudience: Analytics Engineer, Data Analyst, Data Engineer, Business StakeholderExperience level: Beginner, New to dbtBring your own laptop: YesDescription:<ul><li>In this course, we are teaching dbt Fundamentals in person! We will focus on covering the foundation knowledge to get started with dbt and set you on your way to building your very own dbt project. &nbsp;We will cover models, sources, tests, docs, and deployment. &nbsp;You will be provided access to a dbt Cloud account and data warehouse for this training.</li></ul>You’ll learn how to:<ul><li>Build a dbt project in dbt Cloud</li><li>Provide clarity around your project through documentation</li><li>Incorporate dbt best-practices in your project (CTEs, references, etc.)</li></ul>Prerequisites:<ul><li>Basic familiarity with SQL</li><li>No programming experience required</li><li>No experience with data warehouses or dbt required</li></ul>Note:&nbsp;This session requires a premium ticket to attend. If you would like to upgrade to a premium ticket, please contact our team at <a href="mailto:coalesce@getdbt.com">coalesce@getdbt.com</a>.

Bethany is a former High School Math Teacher who’s always loved data and teaching. She joined dbt Lab’s Training team as a Technical Instructor to contribute instructional best practices and lead dbt Learn sessions.

Technical Instructor

Logan joined dbt Labs Training Team as a Technical Instructor in May of 2022. Logan found his passion for education through his experience as a High School Spanish Teacher, and his enthusiasm for data after working in healthcare data analytics. His role here at dbt Labs allows him to combine these two joys as he works with clients to uncover the true power behind the implementation of dbt!

dbt Fundamentals live

Learn about the skills and abilities we seek at dbt Labs! 

•Intro to the dbt Talent Acquisition Team 1:00 - 1:10pm
•Product, Design  - 1:10pm - 1:20pm
•Engineering, Infrastructure, Security - 1:20pm - 1:30pm
•Revenue, Partnerships - 1:30pm - 1:40pm
•Marketing, Community - 1:40pm - 1:50pm
•Customer Support, Professional Services, Training - 1:50pm - 2:00pm
•People Operations - 2:00pm - 2:10pm
•Finance, Accounting - 2:10pm - 2:20pm
•Data - (Internal Data) - 2:20pm - 2:30pm


Learn about the skills and abilities we seek at dbt Labs!&nbsp;<ul><li>Intro to the dbt Talent Acquisition Team 1:00 - 1:10pm</li><li>Product, Design &nbsp;- 1:10pm - 1:20pm</li><li>Engineering, Infrastructure, Security - 1:20pm - 1:30pm</li><li>Revenue, Partnerships - 1:30pm - 1:40pm</li><li>Marketing, Community - 1:40pm - 1:50pm</li><li>Customer Support, Professional Services, Training - 1:50pm - 2:00pm</li><li>People Operations - 2:00pm - 2:10pm</li><li>Finance, Accounting - 2:10pm - 2:20pm</li><li>Data - (Internal Data) - 2:20pm - 2:30pm</li></ul>

Talk Talent to Me

dbt Labs

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. 

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.

Miles is a Sr. Partner Sales Engineer at Snowflake and leads the technical go-to-market and product integration strategy for Snowflake's machine learning and data science partners. Miles has spent the entirety of his career in data science and sales engineering positions and specializes in financial markets and technology industries.

Senior Partner Sales Engineer

Moving to predictive: How to assemble the beginnings of your feature store with Snowflake & dbt Labs

Headline Sponsor

Participants in the dbt Labs partnership program are invited to a partner-only event on Monday, October 17th to hear what’s new on our product and company roadmap, learn about our partner programs, and meet the dbt Labs team and colleagues at other partner companies. 

Participants in the dbt Labs partnership program are invited to a partner-only event on Monday, October 17th to hear what’s new on our product and company roadmap, learn about our partner programs, and meet the dbt Labs team and colleagues at other partner companies.&nbsp;

Partner Day

Data teams are tasked with integrating a growing number of data sources, and enabling broad, self-service access to a consistent and unified view of that data to a growing number of technical and non-technical data consumers for analytics. In this session, learn how dbt and the Dremio open lakehouse platform work together to simplify data architectures, unify data sources, and get insights into the hands of data consumers fast, and how the new connector delivers a seamless user experience across platforms. 

Data teams are tasked with integrating a growing number of data sources, and enabling broad, self-service access to a consistent and unified view of that data to a growing number of technical and non-technical data consumers for analytics. In this session, learn how dbt and the Dremio open lakehouse platform work together to simplify data architectures, unify data sources, and get insights into the hands of data consumers fast, and how the new connector delivers a seamless user experience across platforms.

Jeremiah Morrow is the partner solution marketing director for Dremio. He is responsible for supporting all of Dremio's partner ecosystem, including cloud service providers, technology partners, and channel and systems integrators. Jeremiah joined Dremio in February of this year. Over the past ten years he has worked in partner and industry marketing, analyst relations, sales and business development for technology and advisory companies, including Vertica, OVH, SoftwareONE, and Gartner.

Partner Marketing Director

Build an Open Lakehouse with dbt Labs and Dremio

Contributing to open source can help accelerate your technical learning and be a big career booster, but it can feel intimidating to get started! What if taking the first step is easier than you think? 🤓

Come join us for an afternoon of hacking to kick off Coalesce week with a 💥.

What you can expect:

•friendly guidance on getting started with your first open source contribution
•a room full of other folks who will be excited to 🍐 with you on solving a problem
•exclusive community contributor swag
•… and yes, there will be **prizes ;)
Is this right for me?

Yes! All levels of experience are welcome.


What can I expect to work on?

You can choose to work on anything from dbt Core, to dbt Packages, product documentation, dbt Adapters, the open source dbt metrics package or even blog posts about a problem you solved.

I can’t attend the in person New Orleans event. Can I still participate?

YES! We will be participating in the annual Hacktoberfest for the entire month of October (before AND after Coalesce).

1.Go to any dbt open source repository and look for the “Hacktoberfest” label on an Issue or a GitHub Discussion.
2.If you’re not sure about a particular project to focus on, this is a good place to start and get ideas!
3.Watch this page or #coalesce-hackathon for updates when Hacktoberfest goes live and how to participate!


Contributing to open source can help accelerate your technical learning and be a big career booster, but it can feel intimidating to get started! What if taking the first step is easier than you think? 🤓Come join us for an afternoon of hacking to kick off Coalesce week with a 💥.What you can expect:<ul><li>friendly guidance on getting started with your first open source contribution</li><li>a room full of other folks who will be excited to 🍐&nbsp;with you on solving a problem</li><li>exclusive community contributor swag</li><li>… and yes, there will be **prizes ;)</li></ul>Is this right for me?Yes! All levels of experience are welcome. What can I expect to work on?You can choose to work on anything from dbt Core, to dbt Packages, product documentation, dbt Adapters, the open source dbt metrics package or even blog posts about a problem you solved.I can’t attend the in person New Orleans event. Can I still participate?YES! We will be participating in the annual Hacktoberfest for the entire month of October (before AND after Coalesce).<ol><li>Go to any <a href="https://www.notion.so/Hackathon-Blurb-09a0af4330774379a28545386fd270ee">dbt open source repository</a> and look for the “Hacktoberfest” label on an Issue or a GitHub Discussion.</li><li>If you’re not sure about a particular project to focus on, <a href="https://github.com/dbt-labs/docs.getdbt.com/discussions/">this is a good place to start</a> and get ideas!</li><li>Watch this page or #coalesce-hackathon for updates when Hacktoberfest goes live and how to participate!</li></ol>

Hackathon

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.

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, &nbsp;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.

Eda Johnson is a Principal Data Cloud Architect and Global System Integrator (GSI) Tech Lead at Snowflake. She has 20 years of experience in architecting and implementing advanced analytics solutions in different industry verticals with an extensive knowledge in cloud architecture, data engineering and data science. In her current role, Eda works with our GSI Partners building solutions in the Snowflake Data Cloud.

Lead Partner Sales Engineer

Data Cloud Architect / GSI Partner Sales Engineer

Empowering pythonistas with dbt and snowpark

MDS Ecosystem

Format: In-person

Location: Coalesce New Orleans

Duration: 3 hours (2 hour exam time)

Audience: Analytics Engineer (not already dbt certified)

Experience level: Experienced dbt user

Bring your own laptop: Yes*

Description:

•Live proctored administration of the dbt Analytics Engineering certification exam
Prerequisites:

•None



Note: This session requires a premium ticket to attend. If you would like to upgrade to a premium ticket, please contact our team at coalesce@getdbt.com.

Format: In-personLocation: Coalesce New OrleansDuration: 3 hours (2 hour exam time)Audience: Analytics Engineer (not already dbt certified)Experience level: Experienced dbt userBring your own laptop: Yes*Description:<ul><li>Live proctored administration of the dbt Analytics Engineering certification exam</li></ul>Prerequisites:<ul><li>None</li></ul> Note:&nbsp;This session requires a premium ticket to attend. If you would like to upgrade to a premium ticket, please contact our team at <a href="mailto:coalesce@getdbt.com">coalesce@getdbt.com</a>.

dbt Certification

In this live, instructor-led hands-on lab, you’ll learn how to build a modern data stack with Databricks and dbt, using dbt to manage data transformations in Databricks and perform exploratory data analysis on the clean data sets using Databricks SQL. Based on the lakehouse architecture and built on an open data lake, data analysts, analytics engineers, and data scientists can use dbt and Databricks to work with the freshest and most complete data, and quickly derive new insights for accurate decision-making. 

You’ll learn how to: 

•Quickly connect dbt Cloud and Databricks 
•Model data in Databricks with dbt Cloud 
•Use Databricks SQL to profile and explore your data  


In this live, instructor-led hands-on lab, you’ll learn how to build a modern data stack with Databricks and dbt, using dbt to manage data transformations in Databricks and perform exploratory data analysis on the clean data sets using Databricks SQL. Based on the lakehouse architecture and built on an open data lake, data analysts, analytics engineers, and data scientists can use dbt and Databricks to work with the freshest and most complete data, and quickly derive new insights for accurate decision-making.&nbsp;You’ll learn how to:&nbsp;<ul><li>Quickly connect dbt Cloud and Databricks&nbsp;</li><li>Model data in Databricks with dbt Cloud&nbsp;</li><li>Use Databricks SQL to profile and explore your data &nbsp;</li></ul>

Roberto is a senior solutions architect at Databricks. He loves helping customers build simple and elegant pipelines that enable downstream data analysts and data scientists to unlock the value of their data. Prior to Databricks he was at Mode where he got a front row seat to the mainstream adoption of the modern data stack. In his free time you can find him hiking Angel Island or watch a sports game with friends. 

Senior Solutions Architect

Prasad Kona is a trusted advisor and thought leader who has built and executed complex data analytics and AI strategy for customers and partners. With his deep knowledge around data engineering, data science, machine learning, Prasad, currently working at Databricks, helps customers and partners attain quantifiable business value from their technology investments.

Sr. Partner Solutions Architect

Pradeep is a senior partner solutions architect for ISVs. He helps build and evangelize ISV partner integrations on Databricks Lakehouse platform. Prior to Databricks he was at Booz Allen where he led the team that implemented the enterprise data platform at Centers for Disease Control and Prevention. He has been in the data and analytics space for more than a decade and continues to be a data nerd. In his free time you can find him travelling and hanging out with his family and friends. 

Sr. ISV Partner Solutions Architect

Introducing dbt with Databricks

Creating / migrating metric metadata to dbt can be a pain, because the level of underlying data knowledge required to create the YAML files properly. You might have found yourself wondering, “is this worth it just to standardize metric definitions?”. This talk will tell you why it is definitely worth it… because the functionality you unlock goes beyond just standard metric definitions. Adopting the dbt standard metric syntax unlocks three additional possibilities for your data:  

1. Automated time-aware metric calculations 

2. Dynamic drill downs and segmentation to empower slice and dice analysis  

3. Self-service dynamic transforms using templated SQL 

Creating / migrating metric metadata to dbt can be a pain, because the level of underlying data knowledge required to create the YAML files properly. You might have found yourself wondering, “is this worth it just to standardize metric definitions?”. This talk will tell you why it is definitely worth it… because the functionality you unlock goes beyond just standard metric definitions. Adopting the dbt standard metric syntax unlocks three additional possibilities for your data: &nbsp;1. Automated time-aware metric calculations&nbsp;2. Dynamic drill downs and segmentation to empower slice and dice analysis &nbsp;3. Self-service dynamic transforms using templated SQL

Patrick has solidified himself as a deep domain expert in the data science and data engineering space. He started his career in Data Science at Dell, and later moved into consulting at Slalom where he ultimately led and managed a large practice of Data Scientists and Data Engineers. Patrick has worked with hundreds of organizations and thousands of Data Scientists, consistently seeing that organizations missed their data science objectives due to substantial friction in the data preparation aspects of the lifecycle.Patrick is responsible for the product's strategic direction and manages the product and engineering teams at Rasgo.

Why Metrics Are Even More Valuable Than You Think They Are

The days of unrestrained cloud spending are coming to an end. In the coming months, data teams will increasingly be asked to better understand, monitor and reduce their data warehouse spend. The problem? Practitioners aren't equipped with the tools to do this well. In this talk, Ian Whitestone (Shopify) and Jonathan Talmi (Snapcommerce) will share their own end-to-end process for cutting warehouse spend, diving into the actual strategies they've deployed to give practitioners immediately actionable processes and techniques to keep costs controlled.

Ian’s been working in data for the past 6 years ever since abandoning a career in chemical engineering. He spent his first 3 years at Capital One, where he got to “play” in Teradata, Redshift and Snowflake while the company moved everything to the cloud. He spent the last 3 years at Shopify building and leading a full stack data science team, all while working closely with various business partners to drive value from data. You can learn more about him (https://ianwhitestone.work/about/), his work, and past talks here (https://ianwhitestone.work/talks/).

Jonathan has been working in data for the past 5 years. He’s worked on data teams at Shopify and Instacart, and currently leads a team of Analytics and Data Engineers at Snapcommerce. He’s been a passionate member of the analytics engineering community ever since he led the adoption of dbt at Snapcommerce 3 years ago, and his talk at Coalesce 2021, which showed how to add more observability to dbt, continues to generate a lot of in-bound interest today.

Co-founder

Jonathan is a data professional based in Toronto. He has worked on data teams at Shopify and Instacart, and currently leads a team of analytics and data engineers at Snapcommerce, a mobile commerce company.

 Data Practitioner and Senior Engineering Manager

Extend the runway: A deep dive into data warehouse costs

While the data lakehouse architecture offers many inherent benefits, it’s still relatively new to the dbt community, which creates hurdles to adoption.

In this talk, you’ll meet Apache Hudi, a platform used by organizations to build planet-scale data platforms according to all of the key design elements required by the lakehouse architecture. You’ll also learn how we’ve personaly used Hudi, along with dbt, Spark, Airflow, and many more open-source tools to build a truly reliable big data streaming lakehouse that cut the latency of our petabyte-scale data pipelines from hours to minutes.

While the data lakehouse architecture offers many inherent benefits, it’s still relatively new to the dbt community, which creates hurdles to adoption.In this talk, you’ll meet Apache Hudi, a platform used by organizations to build planet-scale data platforms according to all of the key design elements required by the lakehouse architecture. You’ll also learn how we’ve personaly used Hudi, along with dbt, Spark, Airflow, and many more open-source tools to build a truly reliable big data streaming lakehouse that cut the latency of our petabyte-scale data pipelines from hours to minutes.

Staff Software Engineer

CEO and Founder

Petabyte-scale lakehouses with dbt and Apache Hudi

The Modern Data Stack is becoming more and more fragmented. With new tools and processes popping up continuously, it’s easy to get stranded on various “data islands”, with everything running independently. In this session, we’ll teach you:

 

 - What benefits you gain by turning your Modern Data Stack into an Integrated Data Stack

 - How Shipyard can help you quickly connect the data tools you already use

 - How orchestration is the missing step in your data journey to get your team off “data islands”

 - How dbt fits into the picture of a connected data stack

The Modern Data Stack is becoming more and more fragmented. With new tools and processes popping up continuously, it’s easy to get stranded on various “data islands”, with everything running independently. In this session, we’ll teach you:&nbsp;&nbsp;- What benefits you gain by turning your Modern Data Stack into an Integrated Data Stack&nbsp;- How Shipyard can help you quickly connect the data tools you already use&nbsp;- How orchestration is the missing step in your data journey to get your team off “data islands”&nbsp;- How dbt fits into the picture of a connected data stack

Blake Burch is currently the co-founder and CEO of Shipyard, a modern data orchestration platform that serves as the quickest way for data teams to launch, monitor, and share data workflows. Previously, he was Head of Data for PMG Digital Agency, where he led a team that developed end-to-end data strategy and solutions for brands like OpenTable, Travelocity, Sephora, Cirque Du Soleil, and Gap Inc. Outside of his work, Blake is passionate about new technology, privacy, automation, hiking, science fiction, video games, and board games.

Co-Founder & CEO

Steven is the Data Community Advocate for Shipyard, helping data teams achieve their full potential with powerful data workflows. In his past work, Steven served as a Data Scientist at Cru and a BI Analyst at the Westervelt Company, where he designed ETL processes, made ML models, and built dashboards to accelerate internal initiatives. As a previous math teacher, his passion for helping educate newcomers to understand the complexities of the data field shines through. Steven holds a Masters in Business Analytics and a Masters of Education from the University of Alabama.

Data Community Advocate

Escape from Data Island - Orchestrate and Connect Your Data Stack for Smooth Sailing

So often, data folks find ourselves solving the technical problems (how do we do X, how do we show Y), but forget that data is all about context. Effective use of our data tools goes beyond just picking the best one that has engineering best practices or the most cost effective tool. It is about understanding the people; the audience, the decision makers, the ones implementing the systems and so on. 

Join Lorena Vazquez (Wonder) as she shares tips in creating a more inclusive, compassionate, and empathetic data teams, and how that results in more successful projects.

So often, data folks find ourselves solving the technical problems (how do we do X, how do we show Y), but forget that data is all about context. Effective use of our data tools goes beyond just picking the best one that has engineering best practices or the most cost effective tool. It is about understanding the people; the audience, the decision makers, the ones implementing the systems and so on.&nbsp;Join Lorena Vazquez (Wonder) as she shares tips in creating a more inclusive, compassionate, and empathetic data teams, and how that results in more successful projects.

Lorena has worked in data for over 15 years. She is an accidental data person. Originally, a software engineer, she was introduced into Data Warehousing in the beginning of her career and hasn't looked back. She loves connecting people with data, improving data literacy at all ages, and reducing the gender gap in data. 
She currently leads the BI team at Wonder, a food tech startup changing the way people eat.

BI Lead

Empathy-building in data work

At Preset, we offer a managed service for Apache Superset, the most popular open source business intelligence platform (by Github stars) in the world. We believe the future of BI is not only rooted in open source but also adopts the best ideas from the software development life cycle. To that end, we've created a workflow that enables you to manage Superset datasets, charts, and dashboards as code and we integrated dbt into our platform. In this talk, I'll showcase the speed and change management benefits that are enabled by this workflow of managing core BI assets using dbt and version control. 

At Preset, we offer a managed service for Apache Superset, the most popular open source business intelligence platform (by Github stars) in the world. We believe the future of BI is not only rooted in open source but also adopts the best ideas from the software development life cycle. To that end, we've created a workflow that enables you to manage Superset datasets, charts, and dashboards as code and we integrated dbt into our platform. In this talk, I'll showcase the speed and change management benefits that are enabled by this workflow of managing core BI assets using dbt and version control.

Max is the founder and CEO of Preset, the experts of Apache Superset. Max has over a decade of data engineering experience, at Ubisoft, Lyft, Facebook, and Airbnb. During his time at Airbnb, he created the projects that later spun out of Airbnb and became Apache Airflow and Apache Superset.

How Preset Integrates dbt with Apache Superset to Deliver on Headless BI & Surface Metrics

Welcome Happy Hour

"So what exactly do you do...?" As a data person, you may have been on the receiving end of this question before and that's because the value delivered by data teams is still not clear for many organizations. This makes it challenging to get appropriate investments for data teams to unlock value. Or on the other end of the spectrum, lack of scrutiny of data work can lead to major inefficiencies. 

As data professionals, we should care about the value we create and evangelise our work better. The first step needed is a shared common language between data and its stakeholders. In this talk, Rifat Majumder (Education Perfect) explores a framework that he's been using with executives and business partners to convey the value delivered by data teams. Data leaders can use this to evangelize their team's work and get the right levels of investment they deserve.

"So what exactly do you do...?" As a data person, you may have been on the receiving end of this question before and that's because the value delivered by data teams is still not clear for many organizations. This makes it challenging to get appropriate investments for data teams to unlock value. Or on the other end of the spectrum, lack of scrutiny of data work can lead to major inefficiencies.&nbsp;As data professionals, we should care about the value we create and evangelise our work better. The first step needed is a shared common language between data and its stakeholders. In this talk, Rifat Majumder (Education Perfect) explores a framework that he's been using with executives and business partners to convey the value delivered by data teams. Data leaders can use this to evangelize their team's work and get the right levels of investment they deserve.

Rifat has a varied background, but throughout his career, the common thread has been data. He has held roles in analysis, software, strategy and product. He is excited to see the progression of modern data team practices and technologies to help organisations leverage their data better and faster.

Data & Analytics Lead

Know your worth: Unpacking business value delivered by data teams

As your business and number of product lines grow, the out-of-the-box lead scoring in CRM tools starts becoming difficult to work with and lead scoring becomes that more important for sales teams. Join Ben Lewinsky as he shows how Culture Amp approaches multi-product lead scoring in their data warehouse using dbt.

As your business and number of product lines grow, the out-of-the-box lead scoring in CRM tools starts becoming difficult to work with and lead scoring becomes that more important for sales teams. Join Ben Lewinsky as he shows how Culture Amp approaches multi-product lead scoring in their data warehouse using dbt.

Experience in Rev Ops at multiple SaaS companies in Europe, North America and APAC

Director of Sales Operations.

Why you should not do lead scoring in your marketing automation tools

Without the right discipline and enablement, even a small data-informed startup can quickly become a complex maze of spreadsheets, queries, metrics and analysis. In an organization where access to relevant information is not prioritized, chaos will prevail. Come learn from Karen, a product manager at growing retail startup ALPHA Camp, share the insights she's earned from tackling this problem head on. In this session, she'll share effective strategies for building trust and partnering effectively across business and engineering functions, and how prioritizing structure and shared meaning is where it all starts.

Without the right discipline and enablement, even a small data-informed startup can quickly become a complex maze of spreadsheets, queries, metrics and analysis. In an organization where access to relevant information is not prioritized, chaos will prevail. Come learn from Karen, a product manager at growing retail startup ALPHA Camp, share the insights she's earned from tackling this problem head on. In this session, she'll share effective strategies for building trust and partnering effectively across business and engineering functions, and how prioritizing structure and shared meaning is where it all starts.

A product manager builds company-wide data literacy and empowers the product team to create values for people 👫 and grow 📈 the company to profit.

Agenda