dbt defined an entire new subspecialty of software engineering: Analytics Engineering. But it is one discipline among many: analytics engineers must collaborate with data scientists, data engineers, and data platform engineers to deliver a cohesive data platform. In this video, Nick Schrock of Elementl talks about how orchestrating dbt with Dagster allows you to place dbt in context, de-silo your operational systems, improve monitoring, and enable self-service operations.

Meghan Colón, Head of People Operations with Fishtown Analytics moderates this panel discussion on how to build equitable and inclusive data teams. She is joined by Ilse Ackerman, Director of Data & Analytics with Brooklyn Data Co., Alexis Johnson-Gresham, Engagement Manager also with Brooklyn Data Co., and Colleen Tartow, PhD, Director of Engineering with Starburst Data.

What do you do when data is too messy to be useful, but too large for manual cleaning? In this video, Bladey from Civis Analytics will share their tips for implementing 'human in the loop' data processing — focusing manual efforts on the messiest data. When their team implemented this approach, a data cleaning task that used to take two months was reduced down to two weeks.

Over the last four years, Snaptravel's data team has grown from just one analyst, to almost a dozen, and on the way they've tried five (!) different data team structures. In this session, David Murray will share their journey, and discuss the pros and cons of each structure.

For over a decade, technologists thought that the hard thing about harnessing the exploding amount of data would be about technology: how to store it all, how to process it all, how to analyze it all. Turns out that’s not the hard part. Just as in the wider world, organizations are going through an epistemic crisis: they’re having a hard time knowing what is true and what is false.

Most organizations might not have flat-earthers, fake news, and state-sponsored Twitter bots conducting information warfare, but their challenges determining what’s true are just as existential. Solving them will require good tooling, but even moreso will require a set of core values and supporting cultural norms.

In this video, Tristan Handy, CEO and co-founder of Fishtown Analytics asks: what does that future look like?

Drew Banin, Chief Product Officer and Co-Founder of Fishtown Analytics, reflects on Coalesce 2020, the first (virtual) analytics engineering conference.

Using dbt's metadata on dbt runs (run_results.json) Drizly analytics is able to track, monitor, and alert on its dbt models using Looker to visualize the data. In this video, Emily Hawkins covers how Drizly did this before, using dbt macros and inserts, and how the process was improved using run_results.json in conjunction with Dagster (and teamwork with Fishtown Analytics!)

When we invest time in writing (and speaking!) about our work, we unlock superpowers. We deepen our understanding of processes and practices. We increase efficiency by sharing important information with colleagues. We plant the seeds that help others to grow.

In this video, Janessa Lantz and Stephanie Morillo discuss why you should try technical blogging, how to get started with blogging, and tools for building your personal brand.

You will learn about: - How to pick topics/themes - Finding time in your schedule for writing - Structuring blog posts - Common mistakes and pitfalls - How to maintain momentum

Learn more about Stephanie Morillo at: https://www.stephaniemorillo.co/

Learn more about dbt at: https://getdbt.com https://twitter.com/getdbt

Learn more about Fishtown Analytics at: https://fishtownanalytics.com https://twitter.com/fishtowndata https://www.linkedin.com/company/fishtown-analytics/

Almost all of us are using our data warehouse to power our business intelligence, what if we could use data warehouses do even more?

What if we could use data warehouses to power internal tooling, machine learning, behavioral analytics, or even customer-facing products?

Is this a future we're heading for, and if so, how do we get there?

In this video, you'll join a discussion with speakers: - Boris Jabes, CEO of Census - Jeremy Levy, CEO of Indicative - Arjun Narayan, CEO of Materialize - Jennifer Li, Partner at a16z as moderator

Learn more about the speakers and their companies at: https://www.getcensus.com/ https://www.indicative.com/ https://materialize.com/ https://a16z.com/

Learn more about dbt at: https://getdbt.com https://twitter.com/getdbt

Learn more about Fishtown Analytics at: https://fishtownanalytics.com https://twitter.com/fishtowndata https://www.linkedin.com/company/fishtown-analytics/

There are many ways to do data analysis depending on the needs of the business, the background and experience of the data analyst, and more.

But one thing's for certain: really good data analysis comes down the mastering the basics.

In this video, Kenny Ning (previously at Better.com) takes inspiration from sushi chefs' mastery of making sushi and applies those concepts to data analysis.

You'll learn about the critical concepts to keep your data platform clean and ready for analysis:

1. Know your ingredients = Know where your data comes from
2. Record your recipes = Standardize common logic and documentation
3. Master egg sushi = Focus on the basics of data analysis first

Learn more about dbt at: https://getdbt.com https://twitter.com/getdbt

Learn more about Fishtown Analytics at: https://fishtownanalytics.com https://twitter.com/fishtowndata https://www.linkedin.com/company/fishtown-analytics/

You probably have customer data in your data warehouse — it's a must-have for understanding a business.

This data very likely includes personally identifiable information (PII) which shouldn't be shared with the entire organization.

How do you protect that data and make sure only authorized employees can see that sensitive information?

In this video, you'll learn from Ashley Van Name how JetBlue approaches data protection, particularly the problem of masking PII at scale by leveraging Snowflake's data masking features straight from their dbt project.

Learn more about dbt at: https://getdbt.com https://twitter.com/getdbt

Learn more about Fishtown Analytics at: https://fishtownanalytics.com https://twitter.com/fishtowndata https://www.linkedin.com/company/fishtown-analytics/

In a world where creating new models in as easy as creating new files, and creating links between those models is as easy as typing ref, a directed acyclic graph (DAG) can get pretty unwieldy!

A complex DAG makes it difficult to understand the upstream and downstream dependencies of a particular table.

The goal is to create a modular data model using staging models (base_, stg_) and marts models (int_, dim_, fct_).

In this video, Christine Berger of Fishtown Analytics will teach you how to apply the concepts of layering and modularity to your dbt project, all with a fun kitchen metaphor to keep things fresh!

Learn more about dbt at: https://getdbt.com https://twitter.com/getdbt

Learn more about Fishtown Analytics at: https://fishtownanalytics.com https://twitter.com/fishtowndata https://www.linkedin.com/company/fishtown-analytics/

What happens when two people come to a meeting to talk about business metrics but they have different values for the same metric?

That meeting ends up being spent discussing how the metric was calculated rather than how to impact it.

In this video, you'll learn how the Fishtown Analytics team uses dbt to version control business metrics and create a single source of truth.

You'll also get a framework for how to implement version control for metrics at your organization.

Learn more about dbt at: https://getdbt.com https://twitter.com/getdbt

Learn more about Fishtown Analytics at: https://fishtownanalytics.com https://twitter.com/fishtowndata https://www.linkedin.com/company/fishtown-analytics/

You’re in a state of flow, building out dbt models and then you get the dreaded message — "Quick question about this data..."

As a data team, how do you balance the roadmap work against those "quick" questions?

How do you prioritize all the work you need to do in the short-term (backlog items) while also working on your long-term projects (roadmap items)?

There are advantages to both backlog and roadmap items. How can data teams get the advantages of both?

In this video, Jacob Frackson will show how Data Clinics dedicated time put aside to work on these requests, can help your data team achieve this balance and empower self-serve along the way.

Data clinics have helped an organization: - Deliver 80% of Sprint Points - Answer up to 8 data questions per day - 10x weekly self-serve users on BI tools

Learn more about dbt at: https://getdbt.com https://twitter.com/getdbt

Learn more about Fishtown Analytics at: https://fishtownanalytics.com https://twitter.com/fishtowndata https://www.linkedin.com/company/fishtown-analytics/

What does a data-driven airline look like? How does a data-driven airline behave and treat customers?

JetBlue believes a data-driven airline should: - Offer personalized customer interactions - Predict delays and other "irregular" operations - Enable all analysts to easily access a variety of data sources - Study and monitor operations in real-time to make smarter decisions

The big question is... how does an airline become more data driven?

In this video, Ashley Van Name shares how a small team of data engineers at JetBlue successfully migrated their entire data warehouse workload to dbt and shares tips for setting yourself up for success with dbt.

Fun fact about JetBlue's dbt project — they have 1800 data models, on top of 280 data sources, have defined 8500 tests and they built their entire dbt project in six months!

Learn more about dbt at: https://getdbt.com https://twitter.com/getdbt

Learn more about Fishtown Analytics at: https://fishtownanalytics.com https://twitter.com/fishtowndata https://www.linkedin.com/company/fishtown-analytics/

In this talk, you'll learn how the team at TULA Skincare took a product perspective to the customer journey to understand how customers progress from. basic products to more advanced ones.

It's important to map out the customer journey to understand where they get stuck, where they need help, where the business can improve.

However, when folx talk about mapping a customer’s journey, it's typically only from a marketing perspective. Which channels brought a customer into the funnel? How did they end up converting?

This is important, but that only covers the beginning of the journey where they become a customer. What about the rest of the customer journey where they begin to use your product(s) then go on to buy from you again and again?

What does that customer journey look like?

In this video, Sanjana Sen and Grant Winship of Fishtown Analytics talk through how they approached this exercise while working with the TULA team.

Learn more about dbt at: https://getdbt.com https://twitter.com/getdbt

Learn more about Fishtown Analytics at: https://fishtownanalytics.com https://twitter.com/fishtowndata https://www.linkedin.com/company/fishtown-analytics/

In this introduction to dbt tutorial, you'll to learn about the core concepts of dbt and how it's used.

You probably know that data is a huge part of how the world runs now, including how businesses report on metrics and how they operate.

One of the difficult parts of working with data is communicating enough context and information to everyone in the organization so they understand the data they're looking at and whether it answers their questions.

That's where dbt comes in. dbt is a data transformation and documentation tool that helps data analysts, data engineers, and business stakeholders collaborate on data.

This introduction to dbt will walk you through: a short history of ELT, what is dbt (data build tool), and dbt core concepts.

The core dbt concepts include: - Expressing transforms with SQL select - Automatically build the DAG with ref(s) - Tests ensure model accuracy - Documentation is accessible and easily updated - Use macros to write reusable SQL

Learn more about dbt at: https://getdbt.com https://twitter.com/getdbt

Learn more about dbt Labs (formerly Fishtown Analytics) at: https://www.getdbt.com/dbt-labs/about-us/ https://twitter.com/dbt_labs https://www.linkedin.com/company/dbtlabs