Data Engineering and Cloud Platforms / Module 02 — Data Warehouses and Data Lakes
LOCAL FICTIONAL LEARNING LAB. No website database, cloud account or API key is required.
1. Extract practice_pack.zip into a NEW working folder and preserve an untouched source copy.
2. Read data_dictionary.txt, then attempt learner_worksheet.txt and your own implementation.
3. Reference environment: Python3.12.14. macOS: python3 -m venv .venv; source .venv/bin/activate. Windows: py -m venv .venv; .venv\Scripts\activate.
4. If requirements.txt lists packages: python -m pip install -r requirements.txt. Installation occurs only in your chosen local environment; the website installs nothing.
5. Rename worked_lab.py.txt to worked_lab.py. Command: python worked_lab.py --source . --output run-01
6. A repeat run must use run-02. Existing output directories are refused. result.json is the final completion artifact; a partial folder after an exception is NOT accepted output.
7. Compare expected_results.json only after attempting the tasks. Inspect any generated image/HTML in your browser; dashboard.html has embedded data and is regenerated by the command, not by editing a neighbouring CSV.
8. Optional vendor files require their explicitly named separate tools. The supplied local reference was executed against the included fixture. This does not verify a production service or the learner’s own environment.
9. No arbitrary learner code executes on the production PHP server. No model server/container/cloud resource starts when lesson files are uploaded. API integrations remain disabled.
10. Keep your work locally. Public registration, new paid enrolment and payments remain disabled; protected lessons/downloads require existing approved course access. No accreditation, employment or production approval is implied.