Final answer:
Hadoop is particularly suited for batch processing of large datasets and, with components like Apache Spark, can also handle graph processing. It is not the best choice for real-time processing or small-scale analytics due to its design for large-scale data processing.
Step-by-step explanation:
The Hadoop environment is designed to handle various types of data-processing tasks. Here are the tasks for which you would use Hadoop, based on the given options:
- Batch processing of large datasets and distributed storage. Hadoop's distributed file system (HDFS) and MapReduce programming model are well-suited for processing large volumes of data efficiently.
- Graph processing and in-memory data caching. Although not originally designed for these tasks, Hadoop ecosystem components like Apache Spark can perform in-memory data processing, which can be applied to graph processing tasks.
However, for real-time data processing (a) and small-scale data analytics (b), Hadoop is generally not the best option as it is optimized for large-scale data processing and might not meet the performance requirements of low-latency or smaller-scale computations. Instead, technologies like Apache Storm or Apache Flink for real-time processing, and standalone analytical tools or databases for smaller-scale analytics, would be more appropriate.
Hadoop serves as a computer that can store and perform calculations on large data sets, addressing the need to process a large amount of experimental results on molecules and their motion, as per item (c).