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
Organizational epistemology. Or: How do we know stuff?
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. What does that future look like?
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Last modified on: Apr 19, 2022