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
dbt at the centre of all pipelines
We use dbt not just for [data transformation](https://www.getdbt.com/analytics-engineering/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 my presentation why we do it this way, the pros and the cons of bastardising dbt this way. May also touch on our migration to Snowflake a while ago which allowed us to use dbt this way.
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Last modified on: Nov 23, 2023