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https://github.com/zsvoboda/olympics
Self service analytics of 120 years of Olympics data
https://github.com/zsvoboda/olympics
analytics dashboards data datavisualization dataviz olympics open-data open-datasets opendata reports
Last synced: 24 days ago
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Self service analytics of 120 years of Olympics data
- Host: GitHub
- URL: https://github.com/zsvoboda/olympics
- Owner: zsvoboda
- License: mit
- Created: 2022-02-15T14:21:49.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-03-07T08:11:10.000Z (almost 3 years ago)
- Last Synced: 2024-07-30T19:42:46.792Z (5 months ago)
- Topics: analytics, dashboards, data, datavisualization, dataviz, olympics, open-data, open-datasets, opendata, reports
- Language: Batchfile
- Homepage:
- Size: 7.18 MB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Olympics data
This project downloads 120 years of Olympics data from [this Kaggle dataset](https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results) and transform it to a [GoodData.CN](https://www.gooddata.com/developers/cloud-native?utm_source=mediumcom&utm_medium=referral&utm_campaign=gdcn&utm_content=zd-dbd) semantic data model.
Data are loaded to Postgres database that is part of the GoodData.CN Docker image. This project contains declarative definitions of GoodData metrics, insights, and dashboards that you can import to your local GoodData.CN instance running in a local Docker container.
You'll get this initial Olympics dashboard out of the box:
Then you can create your own data visualizations and interactive dashboards using the visual GoodData.CN analytics tools or APIs. You should be able to have everything up and running in less than 15 minutes.
# Installation steps
1. Install GoodData.CN Community Edition to your computer:
`docker pull gooddata/gooddata-cn-ce`
2. Install [dbd](https://github.com/zsvoboda/dbd) to your computer (make sure that you have Python 3.8+ installed)
```shell
python3 -m venv dbd-env
source dbd-env/bin/activate
pip3 install dbd
```
3. Install [Visual Studio Code](https://code.visualstudio.com) on your computer
Install [REST Client extension](https://marketplace.visualstudio.com/items?itemName=humao.rest-client). You'll use it for the GoodData.CN API invocation.
4. Start GoodData.CN Community Edition
`docker run -t -i -p 3000:3000 -p 5432:5432 --name gd gooddata/gooddata-cn-ce`
Answer 'yes' when prompted.
5. Load the olympics data to the Postgres database that runs in the GoodData.CN container
```shell
cd elt/elt.postgres
dbd run .
```
6. Open this project's files in the Visual Studio Code editor. Invoke the `code` command from the project root directory.
`code .`
7. Load `workspace.code-workspace` file in VSCode (press the blue button in the bottom right area of the file editor)
VSCode should reload.
8. Open the `api/api.postgres/rest.http`
Make sure that your environment is set to `GoodData.CN CE` in the bottom right status bar listbox of the VSCode editor.
9. Create database connection
Find the `# @name createDataSource` on line 11 and click on the small link `Send Request` between line 2 and 3
Sometimes I must click the `Send Request` link twice. A new VSCode editor tab with a good HTTP result code (2xx) should open as result of the invocation.
10. Create physical data model
Find the `# @name storePhysicalModel` on line 31 and click on the small link `Send Request` between line 22 and 23
Again, you may need to `Send Request` link twice.
11. Create workspace
Find the `# @name createWorkspace` on line 36 and click on the small link `Send Request` between line 36 and 37
12. Create semantic model
Find the `# @name publishSemanticModel` on line 62 and click on the small link `Send Request` between line 62 and 63
13. Create analytical objects (dashboards, insights, and metrics)
Find the `# @name createAnalyticsModelObjects` on line 76 and click on the small link `Send Request` between line 76 and 77
14. ENJOY the Olympics analytics at [localhost:3000](http://localhost:3000/)
Username is `[email protected]`, password `demo123`.
You can reach GoodData support on their [Slack community channel](https://www.gooddata.com/slack/?utm_source=mediumcom&utm_medium=referral&utm_campaign=gdcn&utm_content=zd-dbd) if you have a question or run into issues.
There are also useful GoodData University courses available on the [GoodData website](https://www.gooddata.com/learn/?utm_source=mediumcom&utm_medium=referral&utm_campaign=gdcn&utm_content=zd-dbd).