Big data project

Analysis on Million songs Analysis on Million Songs Data set: Spring 2015 - Big Data: Final project The goal of this project assignment is to gain experience in building applications using • Hadoop Open Source Platform • Map reduce programming framework • Hive Query Language • Pig – Latin script Please build the Hadoop cluster with more than one instance on any linux flavored platform. - Download the million songs meta data from the below repository and load the same into HDFS. One good source for download ur data set: [login to view URL] For example please refer the below example file to test your scripts. • [login to view URL] (The above site has files in hdf5 format, you need to convert them to text format before loading into HDFS, totally 26 data sets were available) Please use the letter which has assigned to you and use the corresponding data set for the project work. Please check the below link for the description of each field in dataset. [login to view URL] Submit JPS Output, ifconfig output, Cluster Details & Total number of files count in HDFS. Once the data is loaded successfully into HDFS, please submit the below analytical metrics usingHive, Map reduce or Pig latin. 1. Analyze the Duration of Songs for each year. Submit the calculated results data and also corresponding bar graph / pie chart. 2. Analyze on number of songs for each album available in your data set or Analyze on no of songs which ending with same last digit of their digital ID. Submit the calculated results data and also corresponding bar graph / pie chart. 3. Analyze on number of artists by the first letter of their name OR Analyze the familiarity of song for each year. Submit the calculated results data and also corresponding bar graph / pie chart. 4. Analyze on range of tempo or loud ness for each year. Submit the calculated results data and also corresponding bar graph / pie chart. 5. Analyze on Artists who has more number of songs for each year or Analyze on songs with same key value. Submit the calculated results data.