Notes.txt

For a larger-scale, production-ready ETL pipeline, consider these aspects:

	1.	Scalability:
	•	Use cloud storage solutions like Amazon S3 for incoming files, and a managed SQL database like Redshift for handling larger datasets.
	•	Implement parallel data processing using Spark or a similar framework to handle multiple files and large datasets concurrently.

	2.	Orchestration and Scheduling:
	•	Use an orchestrator (like Airflow) to manage regular ETL job schedules and handle dependencies.
	•	Design the pipeline to handle full data refreshes with each load, or utilize CDC (Change Data Capture) mechanisms if the source allows.
	•	Depending on the use case, ETL runs could be event driven eg. File landing in s3/sftp triggers ETL

	3.	Data Quality:
	•	Validate incoming data against the target schema and log any inconsistencies or missing fields.
	•	Track and report on data quality metrics, such as missing values or format inconsistencies.
	•	Data metrics / counts should be reported and logged at each data hop.
	•	Name, Address cleansing (using besting techniques), persistent unique ID's

	4.	Monitoring and Alerting:
	•	Set up logging and monitoring tools to track ETL performance and notify on failures.
	•	Track processing times and system resource usage for optimization.

	5.	Metadata Management:
	•	Implement a metadata repository to track data lineage, file sources, load times, and any transformations applied.
	• 	Instead of config.py, a more robuts metadata based approach should be used. Example, define each incoming files expected file name, format, layout.
	. 	This metadata can also be defined for inter-depedencies between various feeds.

	6.	Data Governance and Compliance:
	•	Ensure data handling adheres to privacy laws and compliance standards like GDPR, CCPA.
	•	Consider anonymizing or encrypting sensitive data fields.