Scenario: The volume of data processed by your ETL pipeline has increased significantly, leading to longer processing times and resource constraints. How would you redesign the architecture of the ETL system to accommodate the increased data volume while maintaining performance?
- Implement a distributed processing framework such as Apache Spark or Hadoop.
- Optimize network bandwidth and data transfer protocols.
- Scale up hardware resources by upgrading servers and storage.
- Utilize in-memory databases for faster data processing.
To accommodate increased data volume in an ETL pipeline while maintaining performance, implementing a distributed processing framework such as Apache Spark or Hadoop is effective. These frameworks enable parallel processing of data across multiple nodes, improving scalability.
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