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Toggled mach speed instructions for variety of shells (#27)
* Mach speed instructions for variety of shells
* Consistent python reference for pip upgrade
* Apply suggestions from code review
Move the "Prerequisites: Python >= 3.5" directly underneath the "Running this project" H2
Co-authored-by: Kira Furuichi <[email protected]>
* Apply suggestions from code review
Expand the description in the "What's in this repo?" section
Co-authored-by: Kira Furuichi <[email protected]>
Co-authored-by: Kira Furuichi <[email protected]>
Copy file name to clipboardexpand all lines: README.md
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##Testing dbt project: `jaffle_shop`
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# Testing dbt project: `jaffle_shop`
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`jaffle_shop` is a fictional ecommerce store. This dbt project transforms raw data from an app database into a customers and orders model ready for analytics.
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### What is this repo?
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<details>
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<summary>
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## What is this repo?
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</summary>
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What this repo _is_:
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- A self-contained playground dbt project, useful for testing out scripts, and communicating some of the core dbt concepts.
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- CI/CD integrations
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- A demonstration of using dbt for a high-complex project, or a demo of advanced features (e.g. macros, packages, hooks, operations) — we're just trying to keep things simple here!
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### What's in this repo?
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This repo contains [seeds](https://docs.getdbt.com/docs/building-a-dbt-project/seeds) that includes some (fake) raw data from a fictional app.
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</details>
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<details>
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<summary>
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## What's in this repo?
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</summary>
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This repo contains [seeds](https://docs.getdbt.com/docs/building-a-dbt-project/seeds) that includes some (fake) raw data from a fictional app along with some basic dbt [models](https://docs.getdbt.com/docs/building-a-dbt-project/building-models), tests, and docs for this data.
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The raw data consists of customers, orders, and payments, with the following entity-relationship diagram:
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### Why should I care about this repo?
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</details>
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## Why should I care about this repo?
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If you're just starting your cloud data warehouse journey and are hungry to get started with dbt before your organization officially gets a data warehouse, you should check out this repo.
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If you want to run 28 SQL operations with dbt in less than `1 second`, for free, and all on your local machine, you should check out this repo.
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[Verified GitHub Action on dbt Performance](https://github.com/dbt-labs/jaffle_shop_duckdb/runs/7141529753?check_suite_focus=true#step:4:306)
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### Running this project
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## Running this project
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Prerequisities: Python >= 3.5
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### Mach Speed: No explanation needed
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**Mach Speed: No explanation needed**
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> Run `dbt` as fast as possible in a single copy and paste motion!
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dbt docs serve
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```
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### Running `build` steps independently
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## Running `build` steps independently
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1. Load the CSVs with the demo data set. This materializes the CSVs as tables in your target schema. Note that a typical dbt project **does not require this step** since dbt assumes your raw data is already in your warehouse.
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```shell
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