An ETL process is optimized for small datasets but struggles with larger datasets. What adjustments can be made to optimize it for handling large data volumes?
- Implementing row-by-row processing
- Increasing the frequency of data loads
- Removing data validation steps
- Using bulk loading techniques
To optimize an ETL process for handling large data volumes, using bulk loading techniques is crucial. Bulk loading minimizes the overhead associated with processing individual records and allows for faster data transfer and loading, improving overall performance.
Loading...
Related Quiz
- ________ is a process in data validation that involves checking for data type, format, and value constraints.
- BI tools often integrate with ________ to enhance reporting capabilities.
- ________ technologies in BI tools aid in processing large volumes of data efficiently.
- Test requirement analysis should always include a review of the recovery strategy for ETL processes.
- What is the primary goal of Test Data Management in ETL testing?