Which is a commonly cited benefit of Hadoop in handling big data?

Which is a commonly cited benefit of Hadoop in handling big data?

• A. Guarantees real-time processing for all workloads
• B. Eliminates the need for data governance
• C. Increases the speed tasks can be completed
• D. Replaces relational databases entirely

Answer: (C)

Handling big data with Hadoop rests on distributed storage and parallel processing. By spreading data across many machines in HDFS and running tasks concurrently across those nodes, Hadoop achieves high throughput and can complete large analytics tasks much faster than a single machine could. This parallelism is why increasing the speed at which big data tasks are completed is a commonly cited benefit. Real-time processing for all workloads isn’t guaranteed with Hadoop alone—there are startup and processing overheads, and latency can be higher for individual jobs. Governance and integration remain important, and Hadoop doesn’t replace relational databases; it complements them for scalable batch analytics.

Handling big data with Hadoop rests on distributed storage and parallel processing. By spreading data across many machines in HDFS and running tasks concurrently across those nodes, Hadoop achieves high throughput and can complete large analytics tasks much faster than a single machine could. This parallelism is why increasing the speed at which big data tasks are completed is a commonly cited benefit. Real-time processing for all workloads isn’t guaranteed with Hadoop alone—there are startup and processing overheads, and latency can be higher for individual jobs. Governance and integration remain important, and Hadoop doesn’t replace relational databases; it complements them for scalable batch analytics.