Which statement is NOT correct regarding Apache Hadoop?

Which statement is NOT correct regarding Apache Hadoop?

• A. Hadoop is designed for distributed storage and processing
• B. Hadoop is best suited to batch processing
• C. Hadoop is best suited to real-time analytics applications
• D. Hadoop promotes high-throughput access to application data

Answer: (C)

The assertion that Hadoop is best suited to real-time analytics applications is not correct. Apache Hadoop is primarily designed for batch processing rather than real-time analytics. Its architecture focuses on storing and processing large datasets in a distributed manner, making it highly effective for tasks that can be performed in multiple stages over a significant duration, such as ETL processes, large-scale data transformations, and extensive report generation. While there are components within the Hadoop ecosystem, such as Apache Storm or Apache Kafka, that can handle real-time processing, the core Hadoop framework (particularly the MapReduce component) is optimized for batch workloads, leading to higher throughput rather than low-latency, real-time response. Thus, emphasizing Hadoop's strengths aligns more with batch processing and high-throughput scenarios rather than immediate data query responses typically associated with real-time analytics applications.

The assertion that Hadoop is best suited to real-time analytics applications is not correct. Apache Hadoop is primarily designed for batch processing rather than real-time analytics. Its architecture focuses on storing and processing large datasets in a distributed manner, making it highly effective for tasks that can be performed in multiple stages over a significant duration, such as ETL processes, large-scale data transformations, and extensive report generation.

While there are components within the Hadoop ecosystem, such as Apache Storm or Apache Kafka, that can handle real-time processing, the core Hadoop framework (particularly the MapReduce component) is optimized for batch workloads, leading to higher throughput rather than low-latency, real-time response. Thus, emphasizing Hadoop's strengths aligns more with batch processing and high-throughput scenarios rather than immediate data query responses typically associated with real-time analytics applications.