Big Data Processing Frameworks
Description
Comprehensive understanding of distributed computing frameworks including Hadoop ecosystem, Apache Spark, MapReduce, HDFS, and other technologies for processing large-scale datasets.
Learning Objectives
Students will master the architecture and implementation of major big data processing frameworks, design and deploy distributed data processing solutions, optimize performance of big data applications, understand the Hadoop ecosystem components, implement Apache Spark applications for batch and real-time processing, and integrate various big data technologies for comprehensive analytics solutions.
Topics (9)
Comprehensive coverage of Hadoop ecosystem architecture focusing on distributed file system design, data replication, fault tolerance, and cluster management.
Deep dive into MapReduce programming model including job design, optimization techniques, and practical implementation for large-scale data processing.
Comprehensive Apache Spark framework covering core concepts, architecture, programming model, and optimization techniques for unified analytics.
Real-time and near real-time data processing using Apache Spark Streaming for building responsive analytics applications and dashboards.
Event streaming platform fundamentals including Kafka architecture, producer-consumer model, stream processing, and integration with analytics systems.
Non-relational database technologies designed for big data applications including document, key-value, column-family, and graph databases.
Modern deployment strategies for big data applications using containerization and orchestration technologies for improved scalability and resource management.
Advanced techniques for optimizing big data framework performance including memory management, parallel processing tuning, and infrastructure optimization.
Data integration and workflow automation using Apache NiFi for building robust data pipelines and managing data flow in big data architectures.