For a high-volume data ETL process, what best practices should be considered to enhance performance and scalability?
- Aggressive Caching, Real-Time Processing, Data Duplication, Single Node Architecture
- Incremental Loading, In-Memory Processing, Partitioning, Horizontal Scaling
- Pipeline Optimization, Data Compression, Distributed Computing, Waterfall Model
- Vertical Scaling, Batch Processing, Serial Processing, Inefficient Indexing
Best practices for enhancing performance and scalability in a high-volume data ETL process include Incremental Loading, In-Memory Processing, Partitioning, and Horizontal Scaling. Incremental loading reduces the load on systems, and horizontal scaling allows for adding more resources as needed.
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