Introduction

Big Data has become a central focus in industries ranging from healthcare and finance to e-commerce and entertainment. The ability to collect, process, and analyze vast amounts of data is transforming business operations and decision-making. This course introduces the key technologies and tools used in Big Data processing and analysis, offering participants a solid foundation in understanding and working with large datasets. The course covers the concepts, frameworks, and technologies behind Big Data, such as Hadoop, Spark, and NoSQL databases, and provides hands-on experience in working with these tools to process and analyze large-scale data.

Objectives

By the end of this course, participants will:

• Understand the concept of Big Data and its challenges.
• Learn about the Hadoop ecosystem, including HDFS, MapReduce, and YARN.
• Get hands-on experience with Apache Spark for large-scale data processing.
• Understand the importance of distributed computing and parallel processing.
• Learn about NoSQL databases like MongoDB and Cassandra for handling unstructured data.
• Gain familiarity with Big Data tools for data ingestion, storage, processing, and visualization.
• Explore the applications of Big Data technologies in real-world scenarios.

Who Should Attend?

This course is ideal for:

• Data engineers, data scientists, and analysts who want to work with large datasets.
• IT professionals looking to transition into Big Data technologies.
• Business professionals interested in leveraging Big Data for decision-making.
• Students or beginners seeking to learn the fundamental technologies behind Big Data.

Day 1: Introduction to Big Data and Hadoop Ecosystem

Morning Session: What is Big Data?

• Defining Big Data: Volume, Variety, Velocity, and Veracity (4 V’s)
• Challenges of handling Big Data: Storage, scalability, and data quality
• Key differences between traditional databases and Big Data systems
• Applications of Big Data in various industries (e.g., healthcare, finance, marketing)
• Overview of the Big Data ecosystem: Tools, frameworks, and technologies
• Introduction to distributed computing and parallel processing

Afternoon Session: Introduction to Hadoop Ecosystem

• What is Hadoop? History, components, and use cases
• Understanding Hadoop Distributed File System (HDFS): Storage architecture, blocks, replication, and fault tolerance
• Hadoop MapReduce: Parallel processing, programming model, and use cases
• YARN (Yet Another Resource Negotiator): Resource management and job scheduling in Hadoop
• Hands-on: Setting up a basic Hadoop environment and exploring HDFS

Day 2: Working with Hadoop and MapReduce

Morning Session: Data Processing with MapReduce

• Introduction to MapReduce: Concept, input/output, and stages (Map, Shuffle, Reduce)
• Writing MapReduce programs: Mapper and Reducer functions
• Real-world use cases for MapReduce in processing large datasets
• Hands-on: Writing a simple MapReduce program to process text data

Afternoon Session: Advanced Hadoop Components

• Hadoop Common: Libraries and utilities used across the Hadoop ecosystem
• Hadoop Hive: SQL-like queries on Big Data for data warehousing and analysis
• Hadoop Pig: High-level scripting platform for analyzing large datasets
• Hadoop HBase: Columnar NoSQL database for real-time data access
• Hands-on: Querying data using Hive and processing data with Pig

Day 3: Introduction to Apache Spark for Big Data Processing

Morning Session: What is Apache Spark?

• Understanding Apache Spark: Overview, architecture, and components
• Benefits of Spark over Hadoop MapReduce: Speed, ease of use, and in-memory processing
• Spark Core: RDDs (Resilient Distributed Datasets) and transformations/actions
• Spark SQL: Working with structured data using SQL-like queries
• Introduction to Spark MLlib: Machine learning on Big Data
• Hands-on: Running a basic Spark job on a sample dataset

Afternoon Session: Advanced Spark Features

• Spark Streaming: Real-time data processing and stream analytics
• Spark GraphX: Graph processing and analytics
• Spark MLlib: Using Spark for machine learning tasks like classification and regression
• Spark on Cloud: Running Spark on cloud platforms like AWS EMR and Azure HDInsight
• Hands-on: Using Spark SQL and Spark MLlib for basic data analysis and modeling

Day 4: NoSQL Databases and Data Ingestion

Morning Session: Introduction to NoSQL Databases

• What is NoSQL? The rise of NoSQL databases in Big Data processing
• Types of NoSQL databases: Document, key-value, column-family, and graph databases
• Understanding MongoDB: Document-oriented database and use cases
• Understanding Cassandra: Distributed column-family store for handling large datasets
• Hands-on: Setting up a MongoDB database and inserting/retrieving data

Afternoon Session: Data Ingestion and ETL (Extract, Transform, Load)

• Introduction to data ingestion: Techniques for importing Big Data from various sources (e.g., sensors, logs, APIs)
• Batch vs. real-time data processing
• Apache Flume and Apache Kafka for data ingestion in real-time
• ETL pipelines: Tools for data transformation and storage (e.g., Apache NiFi, Talend)
• Hands-on: Setting up a data ingestion pipeline with Apache Kafka

Day 5: Big Data Analytics and Visualization

Morning Session: Big Data Analytics

• Introduction to Big Data analytics: Tools and techniques for analyzing large datasets
• Tools for Big Data analytics: Apache Zeppelin, Jupyter Notebooks, and Tableau for visualization
• Introduction to machine learning on Big Data: Using Spark MLlib and other tools for model building
• Big Data analytics in the cloud: Cloud solutions for storage and processing (e.g., AWS S3, Google BigQuery, Azure Data Lake)
• Hands-on: Analyzing a large dataset using Spark and Apache Zeppelin

Afternoon Session: Big Data in Action and Future Trends

• Real-world use cases: Predictive analytics, recommendation systems, fraud detection, and social media analysis
• Scalability and performance optimization in Big Data processing
• The future of Big Data technologies: AI, edge computing, and Internet of Things (IoT)
• Big Data privacy and security challenges: Ensuring data protection and compliance with regulations (e.g., GDPR)
• Final project: Participants work on a project where they will use Big Data tools to solve a real-world problem

Materials and Tools:

• Software and tools: Hadoop, Spark, MongoDB, Kafka, Hive, Cassandra
• Access to a cloud-based Big Data platform (e.g., AWS, Google Cloud, or Azure) for hands-on exercises
• Recommended readings and resources: Official documentation, tutorials, and case studies on Big Data tools

Conclusion and Final Assessment

• Recap of key concepts: Hadoop, Spark, NoSQL, data ingestion, and Big Data analytics
• Final project: Participants apply what they’ve learned to process and analyze a large dataset
• Certification of completion for those who successfully complete the course and final project