BN4312 Cloud Computing and Big Data Tools for Business Analytics (Laboratory) Syllabus:

BN4312 Cloud Computing and Big Data Tools for Business Analytics (Laboratory) Syllabus – Anna University PG Syllabus Regulation 2021

OBJECTIVES:

➢ To Provide practical insights of cloud computing along with virtualization and cloud computing.
➢ To Provide learns hands on experience in cloud based platforms.

LIST OF EXPERIMENTS FOR CLOUD COMPUTING:

1. Install Virtualbox/VMware Workstation with different flavours of linux or windows OS on top of windows7 or 8.
2. Install a C compiler in the virtual machine created using virtual box and execute Simple Programs.
3. Install Google App Engine. Create hello world app and other simple web applications using python/java.
4. Use GAE launcher to launch the web applications.
5. Simulate a cloud scenario using CloudSim and run a scheduling algorithm that is not present in CloudSim.
6. Find a procedure to transfer the files from one virtual machine to another virtual machine.
7. Find a procedure to launch virtual machine using trystack (Online Openstack Demo Version)
8. Install Hadoop single node cluster and run simple applications like word count.

List of experiments for AWS:
9. Foundational Exercises: AWS Management Console Navigation, Explore the AWS Management Console. Understand how to navigate and use basic features. Setting Up IAM Users and Policies: Create IAM users, groups, and roles. Assign permissions using IAM policies. Set up Multi-Factor Authentication (MFA).
10. Core AWS Services: Amazon EC2 (Elastic Compute Cloud): Launch, connect to, and manage an EC2 instance. Configure security groups and key pairs. Automate deployment using EC2 Auto Scaling. Amazon S3 (Simple Storage Service): Create and configure S3 buckets. Upload, retrieve, and manage objects. Set bucket policies and enable versioning. Amazon RDS (Relational Database Service):Launch and configure an RDS instance. Connect to the database and perform basic operations. Implement automated backups and snapshots.
11. Advanced Services and Scenarios: Amazon VPC (Virtual Private Cloud): Design and configure a VPC with subnets, route tables, and gateways. Set up security groups and network ACLs. Implement VPC Peering.

List of exercises suitable for learning open-source Hadoop:
12. Setting up a Single-Node Hadoop Cluster: Install Hadoop on your local machine and configure it as a single-node cluster. Perform basic file operations on HDFS such as creating directories, uploading files, and listing files.
13. HDFS Operations: Write scripts to automate common HDFS operations such as copying files, moving files, and deleting files. Explore the HDFS file system using command-line tools and perform tasks like checking file status, setting permissions, and changing ownership.
14. Map Reduce Programming: Write a Map Reduce program in Java to count the occurrences of words in a text file. Implement a Map Reduce program to calculate the average temperature from a dataset containing temperature readings. Develop a Map Reduce program to find the most frequent words in a large collection of documents.
15. Map Reduce Optimization: Experiment with different data partitioning techniques and observe their impact on job performance. Implement a Combiner function to optimize the intermediate data processing in a Map Reduce job. Tune the memory configuration and parallelism settings to optimize the performance of a Map Reduce job.
16. Hive Exercises: Create tables in Hive and load data into them from existing datasets. Write Hive QL queries to perform data manipulation tasks such as filtering, sorting, and aggregating. Practice joining multiple tables in Hive to perform more complex analytical queries.

TOTAL : 60 PERIODS

COURSE OUTCOMES:

➢ Acquire practical insights on cloud computing along with virtualization.
➢ Provide students with hands on experience in Big Data and cloud based platforms.

REFERENCES:

1. Paul Zikopoulos, Chris Eaton “Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data”, McGraw Hill, 2012.
2. Paul Zikopoulos, Dirk de Roos, Krishnan Parasuraman, Thomas Deutsch , James Giles, David Corrigan, “Harness the Power of Big data – The big data platform”, McGraw Hill, McGraw-Hill Osborne Media, 2012.
3. Glenn J. Myatt, “Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining”, John Wiley & Sons, Second