Course Curriculum

• 1. Big Data and its core concepts

  • Include Components, characteristics of Big Data and Identify need of cluster computing in processing Big Data

• 2. Python concepts for Big Data processing

  • Setting up Python environment, Illustrate Storing and retrieving data using variables. Use of standard input to store data and Storing and reading data to files in Python

• 3. ETL’s application and implementation in Big Data scenario

  • Implement simple ETL application, solve common programming error while working with data files, Identify Components of MapReduce Paradigm and Practice to work with MapReduce.

• 4. Implement MapReduce framework to process and analyze Big Data

  • Introduce data streams and their benefits to use, set up orchestration environment, split work in cluster computing and demonstrate orchestration technique

• 5. Orchestration in big data context

  • Implementation of distribution in Big Data, Inspect CPU to check if every worker is running as separate, independent process, implement round robin and batch processing technique for process scheduling and use work stealing queue for load balancing.

• 6. MapReduce along with orchestration

  • Split work in cluster computing and Demonstrate orchestration technique

• 7. Amazon Web Service to implement cloud computing in Big Data processing

  • Learn to apply for Amazon account and Access your AWS account.

• 8. Anaconda and Jupyter notebook installation

  • Perform anaconda updates, Setup Jupyter notebook and Start Jupyter Notebook

• 9. Hadoop tool to process and analyze Big Data

  • Launch Amazon EMR cluster, demonstrate add bucket to cluster and add job at AWS cluster.