@Introduetion: |Drganizations often gain data from multiple sources. lter and clean it and then load the data onto different systems for visualizations and analysis. Over time, many ofthese systems may become disconnected and not performing optimally. As a Big Data Developer, you may be tasked with trying to combine data from multiple sources into one or more other sinks. The go~to technologies for transferring data are often Apache Kafka and Spark Streaming. In this Final Project you will mimic an ETL process [short for extra, transform and load) using Spark Streaming and different data sources and sinks giving you practice on how such systems may be developed and implemented. ETL Process The ETL process will have two sources: 1. A Flume agent writing data into an HDFS source. 2. A Kafka producer writing data into a topic on a Kafka server For this assignment, we will use iot data from a Nexus phone (as used in the lecture exercises for Spark Streaming). You can choose other datasets if you wish. Spark streaming will be reading data from the above two sources. The data will then be aggregated and sent to two different sinks depending on the type of data that it is. For any data that involves sitting or standing, the data is sent to a Kafka topic called "idle". For any data other than sitting or standing, the data is sent to a Kafka topic called "active". Two consumers should then read the data and display the activity and time according to the type of activity. Finally, all data must also be sent and saved to a SQL database (for example MySQL)