Course Outline

Module 1: Introduction to Big Data and Distributed Computing

• Overview of Big Data concepts
• Introduction to distributed computing
• Understanding the need for frameworks like Hadoop and Spark
• Case Study/Practical Component: Analyze a real-world scenario where Big Data frameworks like Hadoop and Spark are used to address a specific problem, such as fraud detection in financial transactions.

Module 2: Fundamentals of Hadoop

• Introduction to Apache Hadoop ecosystem
• Hadoop Distributed File System (HDFS)
• MapReduce paradigm for parallel processing
• Hadoop ecosystem components: YARN, HBase, Hive, etc.
• Case Study/Practical Component: Implement a simple MapReduce job to process a large dataset, such as log files, and analyze the results.

Module 3: Setting Up and Working with Hadoop

• Setting up a Hadoop cluster (local or distributed)
• Hands-on exercises with Hadoop streaming and MapReduce jobs
• Data ingestion and storage strategies in Hadoop
• Case Study/Practical Component: Configure a local Hadoop cluster and run a real-world data ingestion scenario, such as importing and processing customer data from CSV files.

Module 4: Introduction to Apache Spark

• Overview of Apache Spark framework
• Key features and advantages of Spark over Hadoop
• Spark architecture: RDDs, DAGs, and execution model
• Case Study/Practical Component: Explore a case where Spark is used to improve performance over Hadoop, such as real-time analytics on user interactions for a website.

Module 5: Spark Programming Fundamentals

• Working with Spark using Scala or Python
• Understanding Spark Context and SparkSession
• Basic transformations and actions in Spark RDDs
• Case Study/Practical Component: Develop a Spark application to perform data aggregation and filtering on a sample dataset, such as analyzing sales data.

Module 6: Data Processing with Spark

• Exploring Spark's DataFrame API
• Data manipulation and transformation using Spark DataFrames
• Introduction to Spark SQL for querying structured data
• Case Study/Practical Component: Use Spark DataFrames and SQL to perform complex queries and transformations on a large dataset, such as customer purchase history.

Module 7: Advanced Spark Techniques

• Introduction to Spark Streaming for real-time data processing
• Machine learning with Spark MLlib
• Graph processing with Spark GraphX
• Case Study/Practical Component: Implement a real-time streaming application using Spark Streaming to process and analyze live social media feeds.

Module 8: Integrating Hadoop and Spark

• Leveraging HDFS for data storage in Spark
• Running Spark on YARN for resource management
• Interacting with Hadoop ecosystem tools from Spark (e.g., Hive, HBase)
• Case Study/Practical Component: Integrate Spark with Hadoop's HDFS and Hive to perform a complex data analysis task, such as querying and processing data from a Hive table.

Module 9: Performance Optimization in Hadoop and Spark

• Techniques for optimizing performance in Hadoop and Spark
• Scaling Hadoop and Spark clusters for large-scale data processing
• Monitoring and tuning Spark applications for efficiency
• Case Study/Practical Component: Optimize a Spark job for performance and scalability, including tuning parameters and analyzing resource usage to handle a large dataset.

Module 10: Advanced Topics and Future Trends in Big Data

• Emerging trends and technologies in Big Data and distributed computing
• Case studies and real-world applications
• Future directions and opportunities in Big Data analytics
• Case Study/Practical Component: Investigate a recent advancement in Big Data technology, such as new tools or techniques, and assess its potential impact on future data analytics practices.