Big Data Infrastructure and Management

Information Technology and Digital Systems October 25, 2025
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Introduction

This advanced course provides comprehensive training in big data infrastructure design, implementation, and management. Participants will learn to work with Hadoop ecosystem components, NoSQL databases, and cloud-based big data platforms. The program covers data ingestion, processing, storage, and analysis at scale, addressing both technical implementation and strategic considerations. Through hands-on exercises and real-world scenarios, attendees will develop the expertise to build and manage big data solutions that enable advanced analytics and business insights from large, complex datasets.

Objectives

Key learning objectives for this course include:

  • Design and implement big data architecture solutions
  • Manage Hadoop ecosystem components and clusters
  • Implement data ingestion pipelines for structured and unstructured data
  • Work with NoSQL databases and distributed storage systems
  • Develop data processing workflows using Spark and other frameworks
  • Implement data governance and security for big data environments
  • Optimize big data platform performance and scalability
  • Integrate big data solutions with existing IT infrastructure
  • Develop strategies for data lake management and dataOps

Target Audience

  • Data Engineers
  • Big Data Architects
  • Data Scientists
  • Infrastructure Engineers
  • IT Managers
  • Database Administrators
  • Solutions Architects
  • Cloud Engineers

Methodology

  • Hands-on cluster configuration exercises
  • Data pipeline development workshops
  • Case studies of big data implementations
  • Performance tuning simulations
  • Cloud platform exploration
  • Group discussions on architecture decisions
  • Individual project work

Personal Impact

  • Enhanced big data architecture and engineering skills
  • Improved distributed systems understanding
  • Stronger data pipeline development abilities
  • Increased confidence in managing large-scale data systems
  • Better cloud platform proficiency
  • Professional growth in data engineering career

Organizational Impact

  • Enhanced analytics capabilities from large datasets
  • Improved data processing efficiency and scalability
  • Better insights from unstructured and real-time data
  • Reduced data storage and processing costs
  • Increased innovation through advanced analytics
  • Stronger competitive advantage through data

Course Outline

Big Data Fundamentals

Core Concepts
  • Big data characteristics and challenges
  • Big data architecture patterns
  • Distributed computing principles
  • Big data use cases and business value
Technology Landscape
  • Hadoop ecosystem overview
  • NoSQL database categories
  • Stream processing platforms
  • Cloud big data services

Hadoop Ecosystem

Core Components
  • HDFS architecture and management
  • YARN resource management
  • MapReduce programming model
  • Hadoop cluster administration
Ecosystem Tools
  • Hive for data warehousing
  • HBase for NoSQL storage
  • Sqoop for data transfer
  • Flume for log collection

Spark Platform

Spark Architecture
  • Spark core concepts and architecture
  • Spark cluster deployment
  • RDD programming model
  • Spark SQL and DataFrames
Advanced Spark
  • Spark Streaming for real-time processing
  • Spark MLlib for machine learning
  • Spark performance tuning
  • Structured Streaming

NoSQL Databases

Database Types
  • Document databases (MongoDB)
  • Column-family stores (Cassandra)
  • Key-value stores (Redis)
  • Graph databases (Neo4j)
Implementation
  • Data modeling for NoSQL
  • Cluster configuration and management
  • Performance optimization
  • Backup and recovery strategies

Data Ingestion & Processing

Data Pipelines
  • Batch vs. stream processing
  • Data ingestion patterns and tools
  • Real-time data processing
  • Data transformation at scale
Workflow Management
  • Workflow scheduling with Airflow
  • Data pipeline monitoring
  • Error handling and recovery
  • Data quality validation

Cloud Big Data Platforms

Platform Services
  • AWS EMR and Redshift
  • Azure HDInsight and Synapse
  • Google BigQuery and Dataproc
  • Multi-cloud strategies
Management & Optimization
  • Cost management and optimization
  • Performance tuning in cloud
  • Security and compliance
  • Hybrid cloud considerations

Data Governance & Operations

Governance Framework
  • Data governance for big data
  • Data catalog implementation
  • Metadata management
  • Data lineage tracking
DataOps Practices
  • DataOps principles and practices
  • CI/CD for data pipelines
  • Monitoring and alerting
  • Disaster recovery planning

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