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https://github.com/malykhin/big_data_training_env
https://github.com/malykhin/big_data_training_env
Last synced: 13 days ago
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- Host: GitHub
- URL: https://github.com/malykhin/big_data_training_env
- Owner: malykhin
- Created: 2020-12-24T04:19:15.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2021-03-09T21:27:38.000Z (over 3 years ago)
- Last Synced: 2023-03-02T06:07:42.931Z (over 1 year ago)
- Language: Shell
- Size: 1.68 MB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
## Before start
1. Install required tools tools:
- make
- docker
- docker-compose
- python
2. Allocate Memory and CPU resources to Docker.
(Preferences... -> Resources)
## General rules:
- Use code formatters and linters while writing the code.
- Apply general good practices like writing dry code, avoiding hard-coding configuration, creating lists of dependencies, etc.
- Leave README.md in the exercise folder with some basic descriptions of how to run your code.
## Services UIs URLs:
- hadoop: http://localhost:9870/
- spark: http://localhost:8081/
- airflow: http://localhost:8080/
- aws: http://localhost:8082/
- superset: http://localhost:8083/
- presto: http://localhost:8084/