List of Experiments / Activities
- BIG DATA AND ANALYTICS LAB
- 1Downloading and installing Hadoop; Understanding different Hadoop modes. Startup scripts, Configuration files.
- 2Implement the following file management tasks in Hadoop: i. Adding files and directories ii. Retrieving files iii. Deleting files Hint: A typical Hadoop workflow creates data files (such as log files) elsewhere and copies them into HDFS using one of the above command line utilities
- 3Implement of Matrix Multiplication with Hadoop Map Reduce
- 4Write a Map Reduce program that mines weather data. Hint: Weather sensors collecting data every hour at many locations across the globe gather a large volume of log data, which is a good candidate for analysis with Map Reduce, since it is semi structured and record-oriented
- 5Run a basic Word Count Map Reduce program to understand Map Reduce Paradigm. 6. Implementation of K-means clustering using Map Reduce
- 7Installation of Hive along with practice examples.
- 8Installation of HBase, Installing thrift along with Practice examples
- 9Patrice importing and exporting data from various data bases .
- 10Write PIG Commands: Write Pig Latin scripts sort, group, join, project, and filter your data.
- 11Run the Pig Latin Scripts to find Word Count .
- 12Run the Pig Latin Scripts to find a max temp for each and every year.
- Note: The Instructor may add/delete/modify/tune experiments
Course Outcomes (COs)
After completion of the course student will be able to:
- CO1Optimize business decisions and create competitive advantage with Big data analyticsK3, K5
- CO2Practice java concepts required for developing map reduce programsK4, K5
- CO3Impart the architectural concepts of Hadoop and introducing map reduce paradigm.K4, K5
- CO4Practice programming tools PIG and HIVE in Hadoop eco system.K5
- CO5Implement best practices for Hadoop development.K5, K6
Official AKTU syllabus
This subject is on page 40 of the original AKTU PDF.