Note: This question is part of a series of questions that present the same Scenario.Each question I the series contains

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Note: This question is part of a series of questions that present the same Scenario.Each question I the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution while others might not have correct solution.Start of Repeated Scenario:initial data that contains the crime data from major cities.You plan to build training models from the training data. You plan to automate the process of adding more data to the training models and to training the models by using the additional data, including data that is collected in near real time. The system will be used to analyze event data gathered from many different sources. Such as Internet of things (IoT) increased crime risk at a particular time and ptace. incoming data stream fromFacebook. which are event-based only, rather than time-based. You also have a time interval stream every 10 seconds. number that defines how many times a hashtag occurs within a Facebook post or how many times a tweet that contains a specific hashtag is retweeted.You must use the appropriate data storage, stream analytics techniques, and AzureHDInsight cluster types tor the various tasks associated to the processing pipeline.End of repeated Scenario.You are designing the real-time portion of the input stream processing. The input will be a continuous stream of data and each record will be processed one at a time. The data will come from an Apache Kafka producer.You need to identify which HDInsight cluster to use for the final processing of the input data. This will be used to generate continuous statistics and real-time analytics. The latency to process each record must be less than one millisecond and tasks must be performed in parallel.Which type of cluster should you identify?

A. Apache Storm
B. Apache Hadoop
C. Apache HBase
D. Apache Spark