Big data use case with servers on-premises

Putting all of this into action, we will develop a fully working system using a data source, a Kafka message broker, an Apache Spark cluster on top of HDFS feeding a Hive table, and a MongoDB database. Our Kafka message broker will ingest data from an API, streaming market data for a Monero (XMR)/Bitcoin (BTC) currency pair. This data will be passed on to an Apache Spark algorithm on HDFS to calculate the price for the next ticker timestamp, based on the following factors:

• The corpus of historical prices already stored on HDFS
• The streaming market data arriving from the API

This predicted price will then be stored in MongoDB using the MongoDB Connector for Hadoop. MongoDB will also receive data straight from the Kafka message broker, storing it in a special collection with the document expiration date set to 1 minute. This collection will hold the latest orders, with the goal of being used by our system to buy or sell, using...