Course

Syllabus

Cloud computing overview: Definitions, benefits, and challenges – Service models: IaaS, PaaS, SaaS – Deployment models: Public, Private, Hybrid, Community – Virtualization: Hypervisors, VM management, Containers (Docker, Kubernetes) – Cloud storage systems: S3, Blob storage, HDFS- Case studies: AWS, Microsoft Azure, Google Cloud Platform

Big Data characteristics: Volume, Velocity, Variety, Veracity, Value – Hadoop Ecosystem: HDFS, MapReduce, YARN, Hive, Pig – Apache Spark: RDDs, DataFrames, MLlib, Streaming – NoSQL Databases: HBase, Cassandra, MongoDB – Data ingestion tools: Flume, Sqoop, Kafka – Hands-on Labs: Basic Hadoop and Spark jobs using sample datasets

Big Data analytics pipeline in the cloud – Data Lake architecture and storage options – Scalable machine learning in the cloud (ML on AWS, Azure ML, Google AI Platform) – Serverless computing and Lambda functions – Real-time analytics using Spark Streaming / Apache Flink – Security, privacy, and compliance in cloud-based big data systems – Case studies: Recommendation engines, IoT analytics, social media mining.

Pre-requisite: Nil

Course Objectives

• To understand the key concepts and architecture of cloud computing platforms and services.
• To explore various cloud service models (IaaS, PaaS, SaaS) and deployment models (Public, Private, Hybrid).
• To study the frameworks and algorithms used in big data processing.
• To develop skills to design and implement scalable cloud-based big data analytics applications.

Course Outcomes

CO1: Understand the architecture and services of cloud computing models.

CO2: Equip knowledge on various cloud platforms like AWS, Azure, and Google Cloud.

CO3: Apply big data frameworks such as Hadoop and Spark for large-scale data processing.

CO4: Design cloud-based systems for scalable storage and analytics.

CO5: Explore challenges in cloud security, resource management, and big data integration.

CO-PO Mapping

1. Buyya, R., Vecchiola, C., & Selvi, S. T., “Mastering Cloud Computing: Foundations and Applications Programming”, 1^st Edition, McGraw-Hill Education, 2013.
2. White, T., “Hadoop: The Definitive Guide, 4^th Edition, O’Reilly Media, 2015.
3. Zaharia, M., Wendell, P., Das, T., & Armbrust, M., “Learning Spark: Lightning-Fast Big Data Analysis”, 1^st Edition, O’Reilly Media, 2015.
4. Bahga, A., & Madisetti, V., “Cloud Computing: A Hands-On Approach”, 1st Edition, Universities Press, 2014.
5. M Sudheep Elayidom, Sarith Divakar M, Lija Mohan, Tanmay Kumar Pandey, Shubham Agrawal, “Cloud Computing & Big Data: From the Basics to Practical Use Cases”, 1^st Edition, 2024.
6. Amazon Web Services (AWS) Documentation – Available at: https://docs.aws.amazon.com
7. Microsoft Azure Documentation – Available at: https://learn.microsoft.com/en-us/azure/
8. Google Cloud Documentation – Available at: https://cloud.google.com/docs