This course provides comprehensive knowledge of big data platforms and processing frameworks for handling massive datasets. Participants will learn to work with distributed computing systems and process large-scale data using modern big data technologies. The curriculum covers Hadoop ecosystem components, Spark processing, NoSQL databases, and cloud-based big data solutions. Through hands-on labs and projects, learners will develop the skills to design and implement scalable data processing pipelines that can handle terabytes of data efficiently.
Big Data Analytics: Platforms and Processing Frameworks
Data Analytics and Business Intelligence
October 25, 2025
Introduction
Objectives
Key learning objectives include:
- Understand big data architecture and ecosystem components
- Implement Hadoop Distributed File System (HDFS)
- Process data using MapReduce and Spark frameworks
- Work with NoSQL databases for big data storage
- Develop scalable data processing pipelines
- Optimize big data workflows for performance
- Implement real-time streaming data processing
- Manage big data clusters and resources
Target Audience
- Data engineers and architects
- Big data developers
- Data scientists working with large datasets
- IT professionals managing data infrastructure
- Software engineers building data-intensive applications
- System administrators
- Cloud data engineers
Methodology
The course uses a combination of theoretical concepts and extensive hands-on labs with big data platforms. Participants work with real large datasets in cloud environments to practice distributed processing. Case studies from web analytics, IoT, and social media provide context for big data applications. Group activities focus on designing scalable architectures, while individual exercises build technical skills. Mini-case studies present specific big data challenges, and syndicate discussions explore solution patterns and best practices.
Personal Impact
- Enhanced ability to work with large-scale data systems
- Improved skills in distributed computing frameworks
- Stronger understanding of big data architecture
- Increased proficiency with cloud data platforms
- Better problem-solving for scalability challenges
- Developed ability to design data processing pipelines
Organizational Impact
- Ability to process and analyze massive datasets
- Improved scalability of data infrastructure
- Reduced processing time for large-scale analytics
- Enhanced capabilities for real-time data processing
- Better cost management for big data workloads
- Increased competitive advantage through big data insights
Course Outline
Unit 1: Big Data Fundamentals
Core Concepts- Characteristics of big data (Volume, Velocity, Variety)
- Big data architecture patterns
- Distributed computing principles
- Big data use cases and business value
Unit 2: Hadoop Ecosystem
Hadoop Core Components- HDFS architecture and operations
- MapReduce programming model
- YARN resource management
- Hadoop cluster administration
- Hive for SQL-like querying
- Pig for data flow processing
- HBase for NoSQL storage
- Sqoop for data transfer
Unit 3: Spark Processing Framework
Spark Fundamentals- Spark architecture and RDDs
- DataFrame and Dataset APIs
- Spark SQL for structured processing
- Spark cluster management
- Spark Streaming for real-time data
- Machine Learning with MLlib
- Graph processing with GraphX
- Performance optimization techniques
Unit 4: NoSQL Databases
NoSQL Categories- Document databases (MongoDB)
- Column-family stores (Cassandra)
- Key-value stores (Redis)
- Graph databases (Neo4j)
Unit 5: Streaming Data Processing
Real-time Analytics- Stream processing concepts
- Apache Kafka for message queuing
- Apache Flink for stream processing
- Storm for real-time computation
Unit 6: Cloud Big Data Platforms
Cloud Solutions- AWS EMR and Athena
- Azure HDInsight and Databricks
- Google BigQuery and Dataflow
- Cloud data lake architectures
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