talk-data.com
Topic
dbt (data build tool)
25
tagged
This video covers what you'll need to build a marketing attribution data model and why it's important to have one to evaluate your business. It covers how the team at Grailed has built data models in an effort to determine where their users come from, where their orders come from, and more. It shares the value these models have unlocked for our marketing team and what it might unlock for yours!
Speaker: Evy Kho, Senior Operations Analyst, Grailed
Have you ever looked at your data and not known where to start? In this video, you'll learn how Firefly Health leveraged Fivetran's dbt packages to quickly transform their raw Salesforce data to analytics-ready models. A process that typically would take weeks was cut down to just minutes with the power of dbt packages.
Speakers: Dom Colyer, Senior Sales Engineer, Fivetran Jacob Mulligan, Head of Analytics, Firefly Health
Now that your team is using dbt, what are the ways you can help your team work even more efficiently? In this video, Bastien Boutonnet and Jean-François Lairie from TripActions share how they supercharged their data team, by using tooling and processes to make the lives of data analysts and data scientists smoother, and to get everyone working like a data engineer (without them even realizing)!
The dbt project at tails.com has over 600 models and 66k lines of code. With multiple contributors to a project and varying SQL backgrounds, it's really difficult to maintain consistent readability and comprehension across a codebase like that by hand.
Python has flake8, Javascript has JSLint, but SQL...?
Listen to this talk from Alan Cruickshank to find out whether SQLFluff might help your teams be more productive with SQL.
Learn how a rapidly growing software development firm transformed their legacy data analytics approach by embracing analytics engineering with dbt and Looker. In this video, Johnathan Brooks of 4 Mile Analytics outlines the complementary benefits of these tools and discusses design patterns and analytics engineering principles that enable strong data governance, increased agility and scalability, while decreasing maintenance overhead.
Jonathan Mak of Nearmap says, "We use dbt not just for data transformation but also data movement in/out of Snowflake. This makes dbt more akin to a generic scheduling and orchestration tool to us and it lives at the centre of our data pipeline. I'd like to discuss in this video why we do it this way, the pros and the cons and may also touch on our migration to Snowflake a while ago which allowed us to use dbt this way.
In this video, President and Founder of Mode, Benn Stancil discusses new ways to align the optimal application boundaries in the modern data stack, providing a set of guidelines for determining how and where to draw the lines between your many tools. He also motivates an example of these boundaries by demonstrating how metadata surfaced in an analytics tool like Mode can increase overall data confidence.
dbt is an essential part of the modern data stack. Over the past four years, the most innovative and forward-thinking data teams have implemented a best-of-breed approach to analytics. This approach has solved many problems, but it has also created new ones. In this video, Drew Banin, Chief Product Officer and co-founder of Fishtown Analytics will share his vision for the data stack of the future.
Effective data governance, lineage and discoverability are key to fully leveraging data within an organization. In this video, Sam Foltin of DTSQUARED will discuss why these processes are so important to a high-functioning data organization, and also share how they are using the metadata artifacts from dbt runs to provide additional insight to inform data governance and discoverability through a dbt integration they've built for Collibra, a metadata management tool.
What does it look like to implement dbt at an organization where the number of employees is in the thousands? In this video we'll learn from the people who have answered exactly this question at organizations like JetBlue and Chesapeake Energy.
Speakers: Chris Holliday (Moderator), Senior VP, Client Management with Visual BI Amy Chen, Solutions Architect with Fishtown Analytics Ryan Goltz, Lead Data Strategist with Chesapeake Energy Ben Singleton, Director of Data Science & Analytics with JetBlue
The team at EQT recently used dbt to fuel an ambitious digital transformation program, and in doing so, used dbt for seven different use cases. In this video, Pietro and Erik will run through those use-cases, and also share how they're using dbt's exposures feature.
How do dbt and Great Expectations complement each other? In this video, Sam Bail of Superconductive will outline a convenient pattern for using these tools together and highlight where each one can play its strengths: Data pipelines are built and tested during development using dbt, while Great Expectations can handle data validation, pipeline control flow, and alerting in a production environment.
Check out the sample repo here: https://github.com/spbail/dag-stack
At many organizations, dbt and the competency of Analytics Engineering are introduced well after the establishment of an analytics team. It's easy to agree in principal with all the benefits and value added by this new tool and analytics practice, but getting there can be a challenge. As with most tool implementations or team restructuring, there is often a long, painful transition from whatever was being done previously to the new future.
In this presentation we'll learn from Andres Recalde's experience implementing analytics engineering practices in both a greenfield situation (La Colombe) and his current successes (and failures!) of implementing analytics engineering at an already established organization (goPuff).
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.
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:
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/