Module 2 introduces students to the challenges of building and managing distributed systems for big data storage and processing. It covers Hadoop’s origins, concepts, core components, and key characteristics, while exploring the Hadoop ecosystem's tools and services. Students will gain an understanding of distributed file systems, specifically HDFS, YARN's resource management, and various technologies for effective big data storage and organization.

In Module 3, students will explore the differences between processing small to moderate versus massive data volumes through distributed computing. This module covers the key concepts of the MapReduce framework, including how it breaks down large data processing tasks into smaller, parallel tasks for efficient execution. Students will also learn about the phases of MapReduce, the role of map and reduce functions, optimization patterns, and the benefits and limitations of various development approaches, including Java-based MapReduce and Hadoop Streaming.

In Module 4, students will explore Apache Spark as a powerful distributed processing framework for interactive, batch, and streaming tasks. This module covers Spark's core functionalities, including machine learning, graph processing, and handling structured and unstructured data, while highlighting its in-memory processing potential and unified nature. Students will compare Spark with MapReduce, learn about Spark's primary components, execution architecture, Resilient Distributed Datasets (RDDs), DataFrames, Datasets, and the various methods for creating and optimizing DataFrames for efficient data processing.

In Module 5, students will delve deeper into Spark's capabilities for data manipulation and transformation. The module covers essential operations such as selecting, filtering, and sorting data, as well as joining DataFrames and performing aggregations. Students will also learn about handling null values, using Spark SQL for data queries, and optimizing performance with caching. Practical applications include creating and manipulating DataFrames, executing transformations and actions, and efficiently writing data to various formats.

Module 6 introduces students to the limitations of batch processing and the significance of real-time data processing. It covers essential aspects of stream processing, including data ingestion and analysis, with a focus on tools like Apache Kafka for stream ingestion and Spark Structured Streaming for scalable and fault-tolerant data processing. Students will also explore various design patterns for organizing big data clusters, the concept of data lakes, and the Lambda Architecture for unifying real-time and batch data processing in modern data environments.

In Module 7, students will explore the benefits and limitations of relational databases in big data contexts and the concept of distributed database systems. This module covers NoSQL databases, their diverse data models, and their scalability and flexibility advantages. Students will also learn about real-world use cases, data partitioning, consistency models, and the CAP Theorem, gaining a comprehensive understanding of how NoSQL databases manage large datasets across clusters while ensuring scalability and availability.

In Module 8, students will explore specific NoSQL databases types – namely Key-Value, Wide-Column, and Document databases. Two similar systems, HBase and Cassandra, will be studied and contrasted in the context of the CAP theorem and associated CP/AP trade-offs. Topics such as consistency and availability will be discussed in the context of specific usage scenarios for both HBase and Cassandra – and general application domains of both systems will be highlighted. Finally, the document database MongoDB will be reviewed in the context of natural language/text processing use cases – and MongoDB usage and architecture will be analyzed with respect to traditional RDBMS.

This module contains the summative course assessment that has been designed to evaluate your understanding of the course material and assess your ability to apply the knowledge you have acquired throughout the course.