Dec 6 - 10, 2021

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Scaling Knowledge > Scaling Bodies: Why dbt Labs is making the bet on a data literate organization

Scaling Knowledge > Scaling Bodies: Why dbt Labs is making the bet on a data literate organization - Erica Louie

Keynote: How big is this wave? Keynote: How big is this wave?

Keynote: How big is this wave? - Martin Casado & Tristan Handy

dbt 101: Stories from real-life data practitioners + a live look at dbt dbt 101: Stories from real-life data practitioners + a live look at dbt

dbt 101: Stories from real-life data practitioners + a live look at dbt - Natty (Jon Natkins) & Alexis Wong Baird

How to build a mature dbt project from scratch

How to build a mature dbt project from scratch - Dave Connors

Analytics Engineering for storytellers

Analytics Engineering for storytellers - Winnie Winship

The modern data experience

The modern data experience - Benn Stancil

Identity Crisis: Navigating the Modern Data Organization Identity Crisis: Navigating the Modern Data Organization Identity Crisis: Navigating the Modern Data Organization Identity Crisis: Navigating the Modern Data Organization Identity Crisis: Navigating the Modern Data Organization

Identity Crisis: Navigating the Modern Data Organization - Jillian Corkin, David Jayatillake, Caitlin Moorman, Barr Moses & Stefania Olafsdottir

Git for the rest of us

Git for the rest of us - Claire Carroll

You don’t need another database: A conversation with Reynold Xin (Databricks) and Drew Banin (dbt Labs) You don’t need another database: A conversation with Reynold Xin (Databricks) and Drew Banin (dbt Labs)

You don’t need another database: A conversation with Reynold Xin (Databricks) and Drew Banin (dbt Labs) - Drew Banin & Reynold Xin

Share. Empower. Repeat. Come learn about how to become a Meetup Organizer!

Share. Empower. Repeat. Come learn about how to become a Meetup Organizer! - Rosie Cardoso

The Operational Data Warehouse: Reverse ETL, CDPs, and the future of data activation

The Operational Data Warehouse: Reverse ETL, CDPs, and the future of data activation - Tejas Manohar

Refactor your hiring process: a framework (Workshop Sponsor) Refactor your hiring process: a framework (Workshop Sponsor) Refactor your hiring process: a framework (Workshop Sponsor)

Refactor your hiring process: a framework (Workshop Sponsor) - Ilse Ackerman, Ezinne Chimah & Rocío Garza Tisdell

Tailoring dbt's incremental_strategy to Artsy's data needs

Tailoring dbt's incremental_strategy to Artsy's data needs - Abhiti Prabahar

Optimizing query run time with materialization schedules

Optimizing query run time with materialization schedules - Ola Canty

How dbt Enables Systems Engineering in Analytics

How dbt Enables Systems Engineering in Analytics - Jorge Cruz Serralles

When to ask for help: Modern advice for working with consultants in data and analytics

When to ask for help: Modern advice for working with consultants in data and analytics - Jacob Frackson

Smaller Black Boxes: Towards Modular Data Products

Smaller Black Boxes: Towards Modular Data Products - Stephen Bailey

The Modern Data Stack: How Fivetran Operationalizes Data Transformations

The Modern Data Stack: How Fivetran Operationalizes Data Transformations - Nick Acosta

Analytics Engineering Everywhere: Why in Five Years Every Organization Will Adopt Analytics Engineering

Analytics Engineering Everywhere: Why in Five Years Every Organization Will Adopt Analytics Engineering - Jason Ganz

Down with

Down with "data science" - Emilie Schario

So You Think You Can DAG: Supporting data scientists with dbt packages

So You Think You Can DAG: Supporting data scientists with dbt packages - Emma Peterson

Operationalizing Column-Name Contracts with dbtplyr

Operationalizing Column-Name Contracts with dbtplyr - Emily Riederer

Data Paradox of the Growth-Stage Startup

Data Paradox of the Growth-Stage Startup - Emily Ekdahl

Batch to Streaming in One Easy Step Batch to Streaming in One Easy Step

Batch to Streaming in One Easy Step - Emily Hawkins & Arjun Narayan

The Call is Coming from Inside the Warehouse: Surviving Schema Changes with Automation The Call is Coming from Inside the Warehouse: Surviving Schema Changes with Automation

The Call is Coming from Inside the Warehouse: Surviving Schema Changes with Automation - Lewis Davies & Erika Pullum

Beyond the Box: Stop relying on your Black co-worker to help you build a diverse team.

Beyond the Box: Stop relying on your Black co-worker to help you build a diverse team. - Akia Obas

Observability Within dbt Observability Within dbt

Observability Within dbt - Kevin Chan & Jonathan Talmi

Inclusive Design and dbt

Inclusive Design and dbt - Evelyn Stamey

Built It Once & Build It Right: Prototyping for Data Teams

Built It Once & Build It Right: Prototyping for Data Teams - Alex Viana

Coalesce After Party with Catalog & Cocktails Coalesce After Party with Catalog & Cocktails

Coalesce After Party with Catalog & Cocktails - Tim Gasper & Juan Sequeda

How to Prepare Data for a Product Analytics Platform (Workshop Sponsor)

How to Prepare Data for a Product Analytics Platform (Workshop Sponsor) - Esmeralda Martinez

Toward a Polyglot Environment for Analytics

Toward a Polyglot Environment for Analytics - Caitlin Colgrove

Automating Ambiguity: Managing dynamic source data using dbt macros

Automating Ambiguity: Managing dynamic source data using dbt macros - Eric Nelson

The Endpoints are the Beginning: Using the dbt Cloud API to build a culture of data awareness

The Endpoints are the Beginning: Using the dbt Cloud API to build a culture of data awareness - Kevin Hu

Data as Engineering

Data as Engineering - Raazia Ali

Building On Top of dbt: Managing External Dependencies

Building On Top of dbt: Managing External Dependencies - Teghan Nightengale

Data Analytics in a Snowflake world: A conversation with Christian Kleinerman and Tristan Handy Data Analytics in a Snowflake world: A conversation with Christian Kleinerman and Tristan Handy

Data Analytics in a Snowflake world: A conversation with Christian Kleinerman and Tristan Handy - Tristan Handy & Christian Kleinerman

Keynote: Building a Force of Gravity

Keynote: Building a Force of Gravity - Drew Banin

dbt Core v1.0 Reveal ✨

dbt Core v1.0 Reveal ✨ - Jeremy Cohen

Firebolt Deep Dive - Next generation performance with dbt Firebolt Deep Dive - Next generation performance with dbt

Firebolt Deep Dive - Next generation performance with dbt - Kevin Marr & Cody Schwarz

dbt, Notebooks and the modern data experience dbt, Notebooks and the modern data experience

dbt, Notebooks and the modern data experience - Allan Campopiano & Elizabeth Dlha

No silver bullets: Building the analytics flywheel No silver bullets: Building the analytics flywheel No silver bullets: Building the analytics flywheel

No silver bullets: Building the analytics flywheel - Kelly Burdine, Lewis Davies & Erika Pullum

Don't hire a data engineer...yet

Don't hire a data engineer...yet - Stefania Olafsdottir

dbt for Financial Services: How to boost returns on your SQL pipelines using dbt, Databricks, and Delta Lake

dbt for Financial Services: How to boost returns on your SQL pipelines using dbt, Databricks, and Delta Lake - Ricardo Portilla

The Future of Data Analytics The Future of Data Analytics The Future of Data Analytics The Future of Data Analytics

The Future of Data Analytics - Sarah Catanzaro, Jennifer Li, Astasia Myers & Julia Schottenstein

Implementing and scaling dbt Core without engineers

Implementing and scaling dbt Core without engineers - Elliot Wargo

Building an Open Source Data Stack

Building an Open Source Data Stack - Katie Hindson

This is just the beginning

This is just the beginning - Alan Cruickshank

dbt in a data mesh world

dbt in a data mesh world - José Cabeda

Introducing the activity schema: data modeling with a single table

Introducing the activity schema: data modeling with a single table - Ahmed Elsamadisi

From Diverse

From Diverse "Humans of Data" to Data Dream "Teams" - Prukalpa Sankar

From 100 spreadsheets to 100 data analysts: the story of dbt at Slido From 100 spreadsheets to 100 data analysts: the story of dbt at Slido From 100 spreadsheets to 100 data analysts: the story of dbt at Slido

From 100 spreadsheets to 100 data analysts: the story of dbt at Slido - Daniela Barokova, Michal Kolacek & Andrej Svec

To All The Data Managers We've Loved Before To All The Data Managers We've Loved Before

To All The Data Managers We've Loved Before - Paige Berry & Adam Stone

Stay Calm and Query on: Root Cause Analysis for Your Data Pipelines (Workshop Sponsor)

Stay Calm and Query on: Root Cause Analysis for Your Data Pipelines (Workshop Sponsor) - Francisco Alberini

Upskilling from an Insights Analyst to an Analytics Engineer

Upskilling from an Insights Analyst to an Analytics Engineer - Brittany Krauth

Modeling event data at scale (Workshop Sponsor)

Modeling event data at scale (Workshop Sponsor) - Will Warner

Building a metadata ecosystem with dbt

Building a metadata ecosystem with dbt - Darren Haken

New Data Role on the Block: Revenue Analytics

New Data Role on the Block: Revenue Analytics - Celina Wong

Using dbt to understand open-source communities

Using dbt to understand open-source communities - Srini Kadamati

Getting Meta about Metadata: Building Trustworthy Data Products Backed by dbt (Workshop Sponsor) Getting Meta about Metadata: Building Trustworthy Data Products Backed by dbt (Workshop Sponsor)

Getting Meta about Metadata: Building Trustworthy Data Products Backed by dbt (Workshop Sponsor) - Angie Brown & Kelechi Erondu

🍪 Eat the data you have: tracking core events in a cookieless world

🍪 Eat the data you have: tracking core events in a cookieless world - Jeff Sloan

Trials and Tribulations of Incremental Models

Trials and Tribulations of Incremental Models - Vincey Au

Sharing the knowledge - joining dbt and

Sharing the knowledge - joining dbt and "the Business" using Tāngata - Chris Jenkins

SQL Draw Artworks Review Panel

SQL Draw Artworks Review Panel - James Weakley

Sharing your work on Discourse

If you’ve done something in your dbt project that you think others can learn from, Discourse is the best place to write about it.

Increasingly, we’re moving towards Discourse being the place that practitioners share their tips and tricks. While Stack Overflow is a great place to ask questions with objectively correct answers, Discourse is more suited to discussing ideas that don’t have one right answer.

Check out some of these existing articles that are well-suited to Discourse:

If you want to share your work on Discourse, here are some common patterns that make an article great. The article, “How to create near real-time views with just dbt + SQL” does almost all of these things perfectly — check it out as a reference!

Share the context of your problem #

It’s useful to know why you took on a particular challenge. This helps readers understand if the article is relevant to them.

Discuss alternative approaches to solving the problem #

There’s usually more than one way to peel a potato, and you may have tried some other things first before settling on the solution. What worked about those, what didn’t?

If there’s relevant open issues in dbt or other discussions that inspired your work, link them here! This might also fit better at the end of the article

Share your solution #

Now for the fun part — showing off your work.

First off, if there’s a concept you need to explain, make sure to do that. This might mean creating a diagram, or including some sample queries.

Then share your solution via code snippets and possibly an example! Consider:

  • Generalizing the code to a familiar, but simpler, example — this can make it easier for someone to understand your work. Check out this example for EAV models — there’s example data + SQL to help readers along the way.
  • Linking to a complete repo with code. We’ve been using this approach more and more, via the labs repo — each sub-directory includes code for demonstrating a solution. By sharing a sample repo, you often get all the benefit of open-source code, with very little maintenance burden (especially compared to maintaining a dbt package). If you go down this route, consider using a seed for mock data and include instructions in the README.
  • Including a Loom video of you walking through the solution — these can be a great way to show multiple aspects at once.

Discuss trade offs #

Often, Discourse articles demonstrate advanced use-cases, which may introduce some complexity to your dbt project. Discussing these tradeoffs helps the reader understand whether the solution is right for them.

Discuss next steps #

Is the problem fully solved? Is there another solution that you haven’t yet tried? Let the community know what you’re thinking and what you’re blocked by. The comments section of Discourse is a great way to get feedback from community members.

Other tips: #

Leverage formatting #

You can write in Markdown on Discourse! Leverage code blocks and headings to help your reader along the way.

Be yourself #

The dbt community is a community of people, not companies, so we encourage you to write as yourself by adding your own voice.

Ask for feedback on a draft #

If this is your first time contributing some technical writing, feel free to share a draft for us to give feedback on. It’s a win-win situation: you’ll get to polish an important skill, and the community gets to benefit from your knowledge!