Scenario: Your team is dealing with a high volume of data that needs to be extracted from various sources. How would you design a scalable data extraction solution to handle the data volume effectively?
- Centralized extraction architectures, batch processing frameworks, data silo integration, data replication mechanisms
- Incremental extraction methods, data compression algorithms, data sharding techniques, data federation approaches
- Parallel processing, distributed computing, data partitioning strategies, load balancing
- Real-time extraction pipelines, stream processing systems, event-driven architectures, in-memory data grids
To design a scalable data extraction solution for handling high data volumes effectively, techniques such as parallel processing, distributed computing, data partitioning strategies, and load balancing should be employed. These approaches enable efficient extraction, processing, and management of large datasets across various sources, ensuring scalability and performance.
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