Data Engineering
Connect the information to a useful decision
We design how records are collected, checked, transformed and delivered to the people or systems that use them. Scheduling, failed loads and changes in source formats are treated as part of the engineering work, not problems to discover later.
Questions behind the data
- Data pipeline design & ETL/ELT development
- Data warehouse & lake architecture
- Data quality & validation frameworks
- Batch & real-time processing
- Pipeline monitoring & alerting
In practice
Data Engineering: a practical example
Follow one source file through the pipeline, including a changed column and an invalid record. Decide how those conditions are reported and who corrects them. Keep a trace from the final dataset to its origin so downstream users can investigate a figure that looks wrong.
An illustrative scenario to discuss when planning your project.