ETL Design and Strategies 🛠️🛤️
Building an ETL pipeline is not just about writing code; it's about strategy. How often do you load? How do you handle errors? Let's look at the design decisions a data engineer must make.
Loading stats…
1. Extraction Methods
- Pull Strategy: The Warehouse asks the source for data (Scheduled).
- Push Strategy: The Source system sends data whenever a transaction happens (Real-time).
- Log-based: Reading the database logs to see exactly what changed.
2. Data Cleaning Strategies
How do we handle "Dirty" data?
- Default Values: If 'Age' is missing, set it to the average age.
- Deduplication: If the same customer is listed twice from two sources, merge them.
- Validation: Check if email addresses have an "@" symbol. If not, mark them as invalid.
3. Data Loading Strategies
- Full Refresh: Wipe the warehouse and reload everything (Only for small data).
- Delta Load (Incremental): Only add the "Delta" (the difference) from the last load.
- Audit Logging: Every ETL run must be logged. If a load fails halfway, the system must know where to restart (Recoverability).
Warning
Performance Impact: The biggest design challenge is ensuring ETL doesn't crash the source systems. Most large companies run their ETL loads at midnight when business activity is lowest.
Summary
- Extraction can be scheduled (Pull) or triggered (Push).
- Cleaning is automated using pre-defined business rules.
- Loading is usually incremental to save time and bandwidth.
- Error Handling is crucial; ETL must be able to recover from crashes.
Quiz Time! 🎯
Loading quiz…