Agenda: Day 2
Don't forget to stop into Jillian's opening remarks in the #coalesce-updates channel!
9 - 9:30am EST | Tailoring dbt's incremental strategy to Artsy's data needs
Abhiti Prabahar (Artsy)
Artsy's data team is a big fan of flexibility. They use dbt to incrementalize long-running tables, while still modeling Artsy's complex business model.
💬Slack: #coalesce-artsy-data-needs
🥔Potato: Incremental potatoes — Potato stacks
9 - 9:30am EST | Refactor your hiring process: a framework (Workshop Sponsor)
Ilse Ackerman, Ezinne Chimah and Rocío Garza Tisdell (Brooklyn Data Co.)
Brooklyn Data Co is in the business of helping companies keep hiring frameworks fresh. They're also hiring on their own team for several positions (job posting below!).
💬Slack: #coalesce-hiring-process
🥔Potato: Don't let them get stale potatoes— Potato chips
10 - 10:30am EST | How dbt Enables Systems Engineering in Analytics
Jorge Cruz Serralles (Updater)
Jorge shows how those coming from a Quality Control mindset can leverage dbt to solve or diagnose common business problems with greater precision.
💬Slack: #coalesce-dbt-systems-engineering
🥔Potato: Ultimate control potato — Sous vide potato
10 - 10:30am EST |Optimizing query run time with materialization schedules
Ola Canty (Firefly Health)
One thing I've noticed about the folks at Firefly Health — they are precise, and practical. Their titles are based on the work to be done (Ola is "Organizer of Data"), and they are REALLY good about measuring impact, like query time reduction.
💬Slack: #coalesce-materialization-schedules
🥔Potato: Fast, practical, reliable potato — Air fryer potato
11 - 11:30am EST | Smaller Black Boxes: Towards Modular Data Products (Premier Sponsor)
Stephen Bailey (Immuta)
The modern data stack has a lot of moving parts. Without proper visibility, documentation, and ownership, things can go wrong fast. Immuta shares how they chopped up their monolithic implementation into well-defined components.
💬Slack: #coalesce-Immuta
🥔Potato: Modular potato products — Homefries
11 - 11:30am EST | When to ask for help: Modern advice for working with consultants in data and analytics
Jacob Frackson (Montreal Analytics)
Learn what questions you should be asking before seeking out consultants, and how to align internal talent with external strengths that best serve your team.
💬Slack: #coalesce-data-consultants
🥔Potato: Sometimes it takes two — Twice-baked potatoes
11:45 - 12:15pm EST | Analytics Engineering Everywhere: Why in Five Years Every Organization Will Adopt Analytics Engineering
Jason Ganz (dbt Labs)
Is there room for an analytics engineer on every data team? Can a single analytics engineer BE the data team? Does company size, maturity, or data literacy matter?
💬Slack: #coalesce-ae-everywhere
🥔Potato: Ubiquitous potato — Fries
11:45 - 12:15pm EST | The Modern Data Stack: How Fivetran Operationalizes Data Transformations (Premier Sponsor)
Nick Acosta and Anna Barr (Fivetran)
Learn how Fivetran uses dbt to power a suite of capabilities like in-warehouse machine learning, predictive modeling, and transformation.
💬Slack: #coalesce-Fivetran
🥔Potato: Piped potatoes — Duchess potatoes
12:30 - 1:15pm EST | Down with "data science"
Emilie Schario (Amplify Partners)
Emilie's 2020 Coalesce talk is the most-watched sessions on the dbt YouTube, with almost 4k views. Today, she's tackling an enormous topic, and it's going to get 🌶️.
💬Slack: #coalesce-down-data-science
🥔Potato: Spicy potato — Patatas Bravas
12:45 - 1:15pm EST | So You Think You Can DAG: Supporting data scientists with dbt packages
Emma Peterson (Civis Analytics)
dbt has over 100 packages designed to speed development—but they also pack a ton of value for non-developers, who benefit from shared context and expertise.
💬Slack: #coalesce-dbt-packages
🥔Potato: Package potato— Papas Rellenas
1:30 - 2:00pm EST | Data Paradox of the Growth-Stage Startup
Emily Ekdahl (Palmetto)
How do growth-stage startups deliver high impact, high velocity, high-quality data products... in highly volatile data contexts? By making some important choices.
💬Slack: #coalesce-growth-startup-data
🥔Potato: Potato with it's whole life ahead of it — Raw potato
1:30 - 2:00pm EST | Operationalizing Column-Name Contracts with dbtplyr
Emily Riederer (Capital One)
Published tables live in a grey area — static enough not to be considered a “service," yet too raw to earn attentive user interface design. This ambiguity creates a disconnect between data producers and consumers. Controlled variables can help.
💬Slack: #coalesce-column-name-contracts
🥔Potato: Potato that is both pasta and a potato — Gnocchi
2:30 - 3:00pm EST | The Call is Coming from Inside the Warehouse: Surviving Schema Changes with Automation
Lewis Davies and Erika Pullum (Aula Education)
Lewis and Erika share how they used dbt to overcame frequent unpredictable schema changes by automating source transformations for similar non-relational sources.
💬Slack: #coalesce-schema-changes
🥔Potato: Potato schemas — Potato skins
2:30 - 3:00pm EST | Batch to Streaming in One Easy Step (Premier Sponsor)
Emily Hawkins and Arjun Narayan (Drizly and Materialize)
Streaming analytics used to require separate knowledge and tooling from batch, but Materialize believes the future of streaming (with a shorter ramp) is dbt and SQL.
💬Slack: #coalesce-materialize
🥔Potato: From batch potato to potato stream — Curly fries
3:15 - 4pm EST | Beyond the Box: Stop relying on your Black co-worker to help you build a diverse team.
Akia Obas (HubSpot)
A hip-hop infused lesson in the importance of data team representation, with a focus on how to proactively build diverse data teams yourself. Hint: It takes time.
💬Slack: #coalesce-diverse-teams
🥔Potato: Put in the time to get it right — Slow cooker potato
3:30 - 4pm EST | Observability Within dbt
Kevin Chan and Jonathan Talmi (Snapcommerce)
Kevin and Jonathan show how they used dbt artifacts to create a central monitoring and alerting system that notified dbt model owners if a specific dbt model failed.
💬Slack: #coalesce-dbt-observability
🥔Potato: Insides showing potato — Jacket potato
4:15 - 4:45pm EST | Build It Once & Build It Right: Prototyping for Data Teams
Alex Viana (HealthJoy)
We've heard "treat your data like a product," via borrowed concepts like testing and version control... but what about prototyping? Why is that harder? Alex shares how to get there, and what's stopped us before.
💬Slack: #coalesce-prototyping
🥔Potato: Potato replication — Tater tots
4:15 - 4:45pm EST | Inclusive Design and dbt
Evelyn Stamey (Civis Analytics)
Evelyn believes dbt is a good example intentional, inclusive design. See what aspects of the product stood out to her, and how to port these paradigms elsewhere.
💬Slack: #coalesce-inclusive-design
🥔Potato: Paradigm potato — Waffle fries
