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

What was your career path into data?

Sent 13 Apr 2021

Hi friends,

What was your career path into data? This is a question we’ve been asking a lot of people lately, and the answers have been really fun! On our team alone, we have scientists, engineers, philosophers, and musicians. Often driven by curiosity, these people learned enough SQL to be dangerous, and ended up finding their way onto a data team.

There’s so much to love about having these diverse career backgrounds on one team, but I’ve recently been wondering why this is the case! Some working theories:

  • Data is a newish field, and college curricula haven’t caught up — as such there is no traditional vocational path onto a data team (say, in the same way that a civil engineer had to study civil engineering). There are some masters degrees, but they often aren't teaching relevant things.
  • It’s hard to learn how to work with data until someone gives you a set of database credentials, and therefore adjacent roles (ops, customer support, finance) end up being funnels into a data team
  • People that aren't on data teams don't really know what exactly it is that data teams do. This is further conflated by the ambiguity around titles on a data team: one team’s data scientist, might be another team’s data analyst, while one team’s data engineer, might be another’s analytics engineer.
  • All of the above? Other things?

I’m curious:

  1. What was your career path into data?
  2. How did you end up learning the skills required on a data team? Are there any resources you’d recommend?
  3. How does the thing you were doing before being on a data team make you a better practitioner?

I might do a roundup in the next newsletter! — Claire, dbt Community Manager

From the dbt community #

Questions, ideas, articles, and useful insights from our community. Many of these discussions take part in the dbt Slack group — you can sign up here.

  • Should your team have one BI tool, or multiple (with each solving a particular use-case)? There was some really great conversation about this topic in our new #bi-tools-general channel.
  • Should everything live in dbt, or should some logic live in your BI tool? A few people weighed in on this thread. (Answer: mostly in dbt!)
  • A few different threads on “Reverse ETL” lately (someone even tried to blame credit me for the “Reverse ETL” name 😬) —why is it called Reverse ETL, and does it replace Fivetran/Stitch? And a writeup from Adam Stone on how Netlify uses "Reverse ETL" in their stack.

A few things you may have missed from us #

  • dbt 0.19.1 was released last week! This is a performance release, with a focus on faster project parsing: ~3x faster, on average. That means less time waiting between typing dbt run and seeing your first model hit the database. Everyone should upgrade, but if your project is on the larger side, it’s probably worth doing it soon! Release notes here.
  • We published our first episode of dbt-tv (dbtv?). Catch the 8 minutes of goodness (or 5 minutes if you watch at 1.5x speed) here
  • I wrote a short article about a technique I use to break down seemingly-complex modeling problems into smaller pieces (what’s the point of having a newsletter if you’re not going to plug your own work 👀). The resulting thread was even better than the article!

Great companies currently hiring #

  • Data Engineer at JetBlue (NYC) 🛫: The team at JetBlue are one of the most advanced teams using dbt, and are blazing the trail on what it means to use dbt at large companies. Want to get to know the team before you apply? Their Manager of Data Engineering, Ashley, gave two incredible talks at Coalesce— one on how JetBlue migrated to dbt, and another on how they secure their PII.
  • Data Analyst at Aula (remote between London and Chicago) 🏫: This startup is doing some really cool things in the edtech space, and is working with our favorite data stack! I really loved this recent article from their Data Lead Kelly Burdine, that debunked the idea that you have to spend 80% of your time cleaning data (hint: use dbt, and then you don’t have to keep re-cleaning!)
  • Senior Analyst at Grailed (NYC) 🛍: My coworker Grant has been working with the Grailed team, so I asked for his input: “Bilal and Seth, Director of Analytics and Senior Data Engineer respectively, have been building a best-in-class dbt-fueled view on online marketplaces for a few years now, and are a genuine delight to work with. If you're passionate about weaving together events, marketing attribution, and customer behavior into compelling narratives, and doing so within a thriving two-sided marketplace, I can't imagine a better role.”
  • A couple of sidestep roles that I think are worth including in case someone is looking for a slight change in their role — product advocate at Firebolt.io (a new data warehouse), Head of Community at Snowplow, and a part time technical writer / developer advocate at PopSQL

Check out the #jobs channel in dbt Slack for more listings (or to add one yourself!). I also heard a rumor that there might be a jobs board, with filters for location and role, coming 🔜.

Upcoming events #

Here’s where community members will be speaking, hosting, or attending. If you have an event to add to list, just reply to this email with the details:

  • April 13: NYC dbt Meetup: I loved the Feb edition of this meetup, hosted by our friends, Brooklyn Data Co (you can catch it here). There’s another one coming up next week, hope to see you there! (And by there, I mean, on Zoom, of course)
  • April 29: London dbt Meetup: We’re hopping across the pond for a meetup in London — we’re still looking to fill a speaker spot, so if you’re UK (or EU) based, and you’re interested in giving a talk, reply to this email to let me know!
  • May 13: Staging: dbt Demo Day: These are quarterly events that bring our community members into the dbt product development lifecycle! We’ll share what our product team has been working on, and hear how some of our community members are using these features in their dbt projects.
  • dbt Learn: We’ve got some courses coming up which are other-side-of-the-world friendly! Check them out here.