Big Data and Hadoop

This course introduces students to the foundational principles of big data, including key concepts such as volume, velocity, variety, and veracity. Students explore the evolution of data management systems and understand how big data technologies differ from traditional databases. The course provides an overview of the big data ecosystem, use cases across industries, and the importance of data-driven decision-making. Emphasis is placed on the practical implications of big data and how organizations can harness large-scale data processing for innovation and efficiency. Learners also examine data governance and ethical considerations when managing extensive datasets.

Students are introduced to the core components of the Hadoop framework, with a focus on the Hadoop Distributed File System (HDFS). The course covers the architecture, configuration, and fault tolerance of HDFS, as well as its role in storing and managing large volumes of data across distributed environments. Learners explore Hadoop ecosystem tools such as YARN, Pig, and HBase, gaining practical knowledge of how each component contributes to scalable data processing. Practical labs reinforce the ability to deploy and manage Hadoop clusters. By the end of the course, students will be able to analyze the suitability of Hadoop solutions for different data challenges.

This course provides an in-depth understanding of MapReduce programming, the foundational data-processing paradigm of the Hadoop ecosystem. Students learn to develop MapReduce applications using Java or other supported languages to perform parallel data processing. Core concepts such as the mapper and reducer functions, job configuration, and optimization techniques are explored in detail. The course also includes practical lab work where students write, execute, and troubleshoot custom MapReduce jobs. Emphasis is placed on designing efficient algorithms for large-scale processing and understanding how to fine-tune jobs for performance in a real-world setting.

Students explore key tools that complement Hadoop’s core capabilities, including Yet Another Resource Negotiator (YARN), Apache Hive, and Apache Pig. The course teaches how YARN manages resources and job scheduling across the cluster, enabling better scalability and performance. Students also learn how Hive simplifies querying large datasets using SQL-like syntax, and how Pig supports procedural data flow programming. Through hands-on labs, learners gain practical experience in designing and executing queries, data transformations, and workflows. The course highlights use cases for each tool and how they integrate to support different analytical tasks in big data environments.

This course focuses on advanced applications of Hadoop and its integration with cloud-based platforms. Students explore how Hadoop is leveraged in real-time analytics, machine learning, and enterprise data lakes. Topics include cloud-native storage integration, Hadoop-as-a-Service offerings, and the use of Hadoop with containerized environments. The course also discusses cost, scalability, and performance considerations when deploying Hadoop in cloud environments. Learners complete lab-based activities to simulate cloud deployment scenarios and hybrid architectures, gaining insight into current industry practices.

This course examines the importance of data governance, privacy, and security in big data ecosystems. Students explore policies, frameworks, and technologies that ensure data integrity, regulatory compliance, and protection against unauthorized access. Topics include data lineage, auditing, role-based access control (RBAC), and encryption within Hadoop environments. The course emphasizes the legal and ethical responsibilities of organizations handling sensitive or personal data. Practical scenarios and case studies help students understand how governance and security measures are implemented and maintained in real-world systems.

This course provides students with a thorough understanding of data warehousing principles and Extract, Transform, Load (ETL) processes in big data environments. Learners explore the architecture of modern data warehouses, including staging areas, data marts, and fact-dimension modeling. The course covers tools and techniques for performing ETL at scale, including batch and stream processing methods. Students gain handson experience with Hadoop-compatible ETL frameworks such as Apache Sqoop, Flume, and Talend. Emphasis is placed on designing efficient ETL workflows to support analytics and reporting needs in enterprise settings.

Students learn how to process data in real-time using technologies that complement the Hadoop ecosystem. This course introduces tools such as Apache Kafka, Apache Storm, and Apache Spark Streaming, focusing on how they ingest, buffer, and analyze streaming data. Topics include event-driven architecture, windowing functions, message queues, and fault tolerance in real-time systems. Through practical labs, students build and deploy realtime data pipelines to handle time-sensitive business insights. The course also examines use cases such as fraud detection, monitoring systems, and dynamic content delivery.

This course teaches students how to extract meaningful insights from large datasets using statistical methods and visualization tools. Learners explore key analytics techniques, including descriptive, predictive, and prescriptive analytics. Visualization tools such as Tableau, Power BI, and open-source alternatives are used to create dashboards, charts, and interactive reports. Students work with big data sources to develop visual narratives that aid in strategic decision-making. Emphasis is placed on data storytelling, user-centric design, and communicating complex insights to non-technical stakeholders.

This course prepares students for relevant industry-recognized certifications in the field of big data and Hadoop. Emphasis is placed on the Cloudera Certified Associate (CCA175) exam, with focused training on core competencies such as data ingestion, transformation, and workflow management using Hadoop tools. Practice exams, study resources, and test-taking strategies are included to ensure student readiness. The course also addresses general exam structures, registration procedures, and continuing education pathways. By the end of the course, students will be equipped with the knowledge and confidence to pursue certification successfully.

In this final course, students apply their cumulative learning to design and implement a comprehensive big data solution. Working individually or in small teams, learners define a real-world data problem, select appropriate tools from the Hadoop ecosystem, and build an end-to-end solution involving data ingestion, processing, analysis, and reporting. The capstone emphasizes project planning, documentation, and presentation of results to stakeholders. Students also reflect on challenges and best practices encountered throughout the project. This course helps bridge the gap between academic learning and professional practice, showcasing student readiness for industry roles.