Duration 5 days – 35 hrs
Overview
This Big Data Training Course provides participants with comprehensive knowledge and practical skills in handling, processing, analyzing, and managing large-scale datasets using modern open source big data technologies.
The course introduces the architecture, ecosystem, and core concepts of Big Data, including distributed storage, distributed computing, real-time processing, data pipelines, analytics, and scalable data engineering solutions. Participants will gain hands-on experience using popular open source platforms such as Apache Hadoop, Apache Spark, Apache Kafka, Hive, HDFS, Airflow, Docker, and related tools commonly used in enterprise data platforms.
The training combines lectures, demonstrations, guided laboratories, workshops, and real-world implementation exercises to help participants understand how organizations process and analyze massive volumes of structured and unstructured data for analytics, reporting, machine learning, and decision-making.
Objectives
- Understand Big Data concepts, architecture, and use cases
- Identify the components of the open source Big Data ecosystem
- Understand distributed computing and distributed storage concepts
- Work with Hadoop Distributed File System (HDFS)
- Process large datasets using Apache Spark
- Perform batch and stream data processing
- Build data ingestion pipelines using Apache Kafka
- Query and analyze data using Hive and Spark SQL
- Understand data orchestration and workflow automation
- Implement scalable Big Data processing solutions
- Explore real-world Big Data architecture and best practices
Target Audience
- Data Engineers
- Data Analysts
- Software Developers
- Database Administrators
- Business Intelligence Developers
- ETL Developers
- DevOps Engineers
- System Administrators
- IT Professionals
- Technical Project Managers
- Professionals transitioning into Big Data and analytics roles
Prerequisites
- Basic knowledge of databases and SQL
- Basic Linux or command-line familiarity
- Understanding of programming concepts is an advantage
- Basic understanding of data processing concepts is beneficial
- Prior experience with Python or Java is helpful but not required
Course Outline
Module 1: Introduction to Big Data
- Understanding Big Data
- Characteristics of Big Data (5 Vs)
- Structured vs Semi-Structured vs Unstructured Data
- Traditional Data Processing vs Big Data
- Big Data Use Cases Across Industries
- Introduction to Distributed Computing
- Overview of Big Data Architecture
- Open Source Big Data Ecosystem Overview
Hands-On
- Exploring sample big data environments
- Introduction to Linux command-line basics
Module 2: Big Data Architecture and Ecosystem
- Components of Big Data Platforms
- Distributed Storage Concepts
- Distributed Processing Concepts
- Data Ingestion and Data Pipelines
- Batch Processing vs Stream Processing
- Data Lakes and Lakehouse Concepts
- Cloud vs On-Premise Big Data Architecture
Open Source Technologies Covered
- Apache Hadoop
- Apache Spark
- Apache Kafka
- Apache Hive
- Apache Airflow
- Docker
Hands-On
- Setting up a local Big Data lab environment using Docker
Module 3: Hadoop Fundamentals and HDFS
- Introduction to Apache Hadoop
- Hadoop Architecture
- Hadoop Ecosystem Components
- Hadoop Distributed File System (HDFS)
- NameNode and DataNode Concepts
- Data Replication and Fault Tolerance
- Managing Files in HDFS
- Hadoop Cluster Concepts
Hands-On
- Installing Hadoop environment
- Working with HDFS commands
- Uploading and managing files in HDFS
Module 4: Data Processing with Apache Spark
- Introduction to Apache Spark
- Spark Architecture
- Spark Components
- Resilient Distributed Datasets (RDD)
- DataFrames and Datasets
- Spark Transformations and Actions
- Spark SQL Fundamentals
- Spark Performance Optimization Basics
Hands-On
- Running Spark applications
- Processing datasets using Spark SQL
- Data transformation exercises
Module 5: SQL Analytics with Apache Hive
- Introduction to Apache Hive
- Hive Architecture
- Hive Tables and Partitions
- HiveQL Fundamentals
- Querying Large Datasets
- Data Warehousing Concepts in Hive
- Optimizing Hive Queries
Hands-On
- Creating Hive databases and tables
- Running analytical SQL queries
- Loading and querying big datasets
Module 6: Real-Time Data Streaming with Apache Kafka
- Introduction to Data Streaming
- Apache Kafka Fundamentals
- Kafka Architecture
- Producers and Consumers
- Topics and Partitions
- Real-Time Data Pipelines
- Event-Driven Architecture Concepts
Hands-On
- Setting up Kafka environment
- Sending and consuming streaming messages
- Building simple streaming pipelines
Module 7: Workflow Orchestration and Automation
- Introduction to Data Workflow Automation
- Apache Airflow Fundamentals
- DAG Concepts
- Scheduling and Monitoring Pipelines
- Managing ETL Workflows
- Error Handling and Notifications
Hands-On
- Creating Airflow workflows
- Scheduling automated jobs
- Monitoring data pipelines
Module 8: Data Analytics and Visualization
- Big Data Analytics Concepts
- Exploratory Data Analysis
- Data Aggregation Techniques
- Reporting and Dashboarding
- Introduction to Data Visualization
- Integrating Big Data with BI Tools
Open Source Tools Covered
- Apache Superset
- Metabase
Hands-On
- Connecting BI tools to data sources
- Creating dashboards and reports
Module 9: Big Data Security, Governance, and Optimization
- Big Data Security Concepts
- Access Control and Authentication
- Data Governance Fundamentals
- Backup and Recovery Concepts
- Monitoring Big Data Environments
- Cluster Performance Optimization
- Scalability Best Practices
- High Availability Concepts
Hands-On
- Monitoring cluster performance
- Resource optimization exercises
Module 10: Modern Big Data Platforms and Capstone Project
- Data Lake and Lakehouse Architectures
- Introduction to Delta Lake Concepts
- Big Data and AI/ML Integration
- Real-World Big Data Architectures
- Industry Best Practices
- Enterprise Big Data Design Considerations
