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README.md

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@@ -6,15 +6,15 @@ This repository consists of a [dbt](https://www.getdbt.com/) project that transf
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To learn more about the overall architecture design & strategy can be found in our centralized handbook:
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- [Data Architecture Handbook](https://docs.google.com/document/d/1WmOnx_5QaGmo-kNWitd9jSEwvEnwqxiS0ZA1jmMV-v0/edit?usp=sharing)
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## ** Sources: **
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## Sources:
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Raw, unformatted data loaded directly from source systems using various data tools.
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- `nba_data` - The primary source of NBA statistics data captured from an API & loaded via Sling/Airbyte.
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- Schema: `analytics.raw_nba_data`
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- `google_sheets` - Internally maintained reference sheets related to the project & loaded via Sling/Airbyte.
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- Schema: `analytics.raw_google_sheets`
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### Environments:
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## Environments:
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Transformed data models built via dbt with 3 distinct environments to enable a sustainable development workflow.
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- **Development**
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- Schema: `analytics.dev_[your-name]`
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- `analytics.marts`
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- Separation by layer for easier navigation and permission management.
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### How to Get Started?
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## How to Get Started?
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1. Create your local development environment
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- Use a local IDE (ex. VS Code), dbt Cloud or GitHub Codespaces
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2. Clone the current repo (or create a new one)
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- Run "git pull" to sync the latest version of the code
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8. Continue to develop & repeat the process
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### Notes
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## Notes
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- Provide any other important call-outs of platform-specific information here.
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### Resources:
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## Resources:
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- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)
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- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers
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- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support

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