Table of Contents
- Data transformation
- • Kiro Dreams of Data
- • dbt 101 (EU and US-friendly)
- • Ask the experts: Customer product journey
- • How to build a reputation on more than just dashboards
- • Ask the experts: Version controlling your metrics
- • How to complete a data modeling project at a $6B company
- • Kimball in the context of the modern data warehouse: what's worth keeping, and what's not
- • Seven use cases for dbt
- • Quickstart your analytics with Fivetran dbt packages
- • Building a marketing attribution model with dbt
- Data testing
- Implementations + deployment
- Documentation + metadata
- The modern data stack
- Data dream teams
- • Lessons in prioritization: How to balance the 'quick questions' against your team's long-term plan
- • Getting started with technical blogging
- • How to structure a data team
- • Run your data team as a product team
- • Hiring a diverse data team
- • How to start your analytics engineering team
- • Supercharging your data team
- • Balancing creativity and proficiency as a data team
- • Evaluating an offer in the data space
- • Taking off with dbt: JetBlue's dbt journey!
- • Data dream teams: TripActions
- • Data dream teams: Netlify
Auditing model layers and modularity with your DAG
In a world where creating new models in as easy as creating new files, and creating links betwen those models is as easy as typing `ref`, [DAG](https://docs.getdbt.com/terms/dag) can get... pretty unwieldy! In this session, we'll learn how to apply the concepts of layering and modularity to your dbt project, all with a fun kitchen metaphor to keep things fresh!
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Last modified on: Nov 23, 2023