https://github.com/teradata/dbt-jaffle-lake
https://github.com/teradata/dbt-jaffle-lake
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/teradata/dbt-jaffle-lake
- Owner: Teradata
- License: apache-2.0
- Created: 2023-10-10T14:25:24.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-08T12:32:36.000Z (over 2 years ago)
- Last Synced: 2025-04-13T09:14:19.474Z (about 1 year ago)
- Size: 63.5 KB
- Stars: 1
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Jaffle Lake
`jaffle_lake` is a fork of the original jaffle_shop dbt demo project modified to run on a VantageCloud Lake Instance.
## Prerequisites
- An instance of VantageCloud Lake
- The jaffle_lake database (it is created through the query in the references folder)
- A user with access to:
- Create compute cluster
- Create table
- Create view
## Configuration
- The profile included in the profiles.yml.template, completed with the actual configurations, should be copied to the profiles.yml file on ~/.dbt.
## dbt models for `jaffle_lake`
`jaffle_lake` is a fictional ecommerce store. This dbt project transforms raw
data from an app database into a customers and orders model ready for analytics.
The raw data from the app consists of customers, orders, and payments, with the
following entity-relationship diagram:
This [dbt](https://www.getdbt.com/) project has a split personality:
* **Tutorial**: The [tutorial](https://github.com/fishtown-analytics/jaffle_lake/tree/master)
branch is a useful minimum viable dbt project to get new dbt users up and
running with their first dbt project. It includes [seed](https://docs.getdbt.com/reference#seed)
files with generated data so a user can run this project on their own warehouse.
* **Demo**: The [demo](https://github.com/fishtown-analytics/jaffle_lake/tree/demo/master)
branch is used to illustrate how we (Fishtown Analytics) would structure a dbt
project. The project assumes that your raw data is already in your warehouse,
so therefore the repo cannot be run as a standalone project. The demo is more
complex than the tutorial as it is structured in a way that can be extended for
larger projects.
### Using this project as a tutorial
To get up and running with this project:
1. Install dbt using [these instructions](https://docs.getdbt.com/docs/installation).
2. Clone this repository. If you need extra help, see [these instructions](https://docs.getdbt.com/docs/use-an-existing-project).
3. Change into the `jaffle_lake` directory from the command line:
```bash
$ cd jaffle_lake
```
4. Set up a profile called `jaffle_lake` to connect to a data warehouse by
following [these instructions](https://docs.getdbt.com/docs/configure-your-profile).
If you have access to a data warehouse, you can use those credentials – we
recommend setting your [target schema](https://docs.getdbt.com/docs/configure-your-profile#section-populating-your-profile)
to be a new schema (dbt will create the schema for you, as long as you have
the right priviliges). If you don't have access to an existing data warehouse,
you can also setup a local postgres database and connect to it in your profile.
5. Ensure your profile is setup correctly from the command line:
```bash
$ dbt debug
```
6. 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.
```bash
$ dbt seed
```
7. Run the models:
```bash
$ dbt run
```
> **NOTE:** If this steps fails, it might be that you need to make small changes to the SQL in the models folder to adjust for the flavor of SQL of your target database. Definitely consider this if you are using a community-contributed adapter.
8. Test the output of the models:
```bash
$ dbt test
```
9. Generate documentation for the project:
```bash
$ dbt docs generate
```
10. View the documentation for the project:
```bash
$ dbt docs serve
```
### What is a jaffle?
A jaffle is a toasted sandwich with crimped, sealed edges. Invented in Bondi in 1949, the humble jaffle is an Australian classic. The sealed edges allow jaffle-eaters to enjoy liquid fillings inside the sandwich, which reach temperatures close to the core of the earth during cooking. Often consumed at home after a night out, the most classic filling is tinned spaghetti, while my personal favourite is leftover beef stew with melted cheese.
---
For more information on dbt:
- Read the [introduction to dbt](https://dbt.readme.io/docs/introduction).
- Read the [dbt viewpoint](https://dbt.readme.io/docs/viewpoint).
- Join the [chat](http://slack.getdbt.com/) on Slack for live questions and support.
---