This markdown explains the full implementation demonstrated in airflow.example.ipynb. It simulates a local execution of the Bitcoin data pipeline using utility functions designed to be compatible with Apache Airflow.
The notebook simulates an end-to-end pipeline execution manually, offering:
- Local testing for Airflow-compatible components
- Visualization of time-series analytics
- Insight into anomaly detection and alerting logic
- Environment variables for file paths are configured.
bitcoin_utils.pyis reloaded withimportlibfor dynamic updates.- Logging is enabled for consistent traceability.
fetch_bitcoin_price()retrieves live Bitcoin pricing from CoinGecko.- Logs details like price and percentage changes (1h/24h).
- Demonstrates real-time ingestion.
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save_price_to_csv():- Appends new price to
bitcoin_raw.csv - Detects abnormal price changes
- Sends Slack alerts for anomalies
- Appends new price to
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archive_raw_snapshot():- Archives current snapshot with a timestamp
- Optionally uploads to S3 for historical analysis
-
compute_moving_average(window=3):- Computes moving average and standard deviation
- Saves and optionally uploads
bitcoin_processed.csv
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Multiple Matplotlib plots showcase:
- Raw prices with moving average and std deviation
- Annotated timestamps
- Anomaly thresholds in price change metrics
- Final result is uploaded to AWS S3 via
upload_to_s3() - Confirms compatibility with cloud deployment
This notebook leverages modular logic from bitcoin_utils.py to:
- Reduce redundancy
- Promote consistency across DAGs and notebooks
- Improve testability and debugging
The pipeline emphasizes:
- Modularity: All logic lives in reusable utility functions
- Observability: Clear logs and alerting for monitoring
- Reproducibility: Timestamped archives and visual tracking
- Scalability: Seamless shift from local test to Airflow deployment
For deeper insight into the API layer, refer to airflow.API.md.