Course Overview

Course Description:Understand data fundamentals, key tools like Excel & SQL, and explore data collection, cleaning, and visualization techniques.

Topics Covered:

• Fundamentals of big data and its characteristics
• Introduction to big data technologies (Hadoop, Spark)
• Basic data storage and processing techniques
• Overview of data ingestion and ETL processes
• Simple data analysis and visualization

Syllabus

Module 1: Introduction to Big Data & Analytics

• What is Big Data? 5 Vs (Volume, Velocity, Variety, Veracity, Value)
• Big Data Ecosystem & Career Scope
• Traditional Databases vs Big Data Systems

LAB 1

• Explore Kaggle Datasets
• Perform Exploratory Data Analysis using Python (Pandas + Matplotlib)

Module 2: Data Collection & Ingestion

• Data Sources (Logs, IoT, Social Media, Sensors)
• Batch vs Streaming Data Ingestion
• ETL Concepts (Extract, Transform, Load)

LAB 2

• Use Python Requests + APIs to Collect Data
• Ingest Streaming Data using Kafka Local Setup

Module 3: Hadoop Ecosystem Basics

• HDFS (Hadoop Distributed File System)
• MapReduce Programming Model
• Hadoop Ecosystem (Hive, Pig, HBase, Sqoop)

LAB 3

• Install Hadoop Single-Node Cluster (Local VM or Docker)
• Run HDFS File Operations (Upload, List, Read, Delete)
• Execute a Simple MapReduce Word Count Job

Module 4: Data Warehousing & Hive

• Introduction to Hive & HQL (Hive Query Language)
• Data Warehousing Concepts
• Partitioning & Bucketing

LAB 4

• Install Apache Hive
• Run SQL Queries on Hive Tables (SELECT, GROUP BY, JOIN)

Learning Outcome

Understand core big data concepts, use basic big data technologies, manage data storage and processing, and perform introductory data analysis and visualization.

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Course Overview

Course Description: Learn to work with Hadoop, Spark, and NoSQL. Perform large-scale data processing and uncover insights using analytics tools.

Topics Covered:

• Advanced data processing with Apache Spark
• Data warehousing and management with Hadoop
• Real-time data processing and streaming
• Data integration and ETL pipelines
• Basic data modeling and analytics

Syllabus

Module 1: Introduction to Big Data & Analytics

• What is Big Data? 5 Vs (Volume, Velocity, Variety, Veracity, Value)
• Big Data Ecosystem & Career Scope
• Traditional Databases vs Big Data Systems

LAB 1

• Explore Kaggle Datasets
• Perform Exploratory Data Analysis using Python (Pandas + Matplotlib)

Module 2: Data Collection & Ingestion

• Data Sources (Logs, IoT, Social Media, Sensors)
• Batch vs Streaming Data Ingestion
• ETL Concepts (Extract, Transform, Load)

LAB 2

• Use Python Requests + APIs to Collect Data
• Ingest Streaming Data using Kafka Local Setup

Module 3: Hadoop Ecosystem Basics

• HDFS (Hadoop Distributed File System)
• MapReduce Programming Model
• Hadoop Ecosystem (Hive, Pig, HBase, Sqoop)

LAB 3

• Install Hadoop Single-Node Cluster (Local VM or Docker)
• Run HDFS File Operations (Upload, List, Read, Delete)
• Execute a Simple MapReduce Word Count Job

Module 4: Data Warehousing & Hive

• Introduction to Hive & HQL (Hive Query Language)
• Data Warehousing Concepts
• Partitioning & Bucketing

LAB 4

• Install Apache Hive
• Run SQL Queries on Hive Tables (SELECT, GROUP BY, JOIN)

Module 5: Data Processing with Apache Pig & HBase

• Pig Latin Basics
• NoSQL Databases: HBase Overview
• HBase Data Model & CRUD Operations

LAB 5

• Run Pig Latin Scripts for Log Processing
• Create an HBase Table & Insert/Query Data

Module 6: Apache Spark for Big Data

• Spark Architecture & RDDs
• DataFrames & Datasets
• Spark SQL Basics

LAB 6

• Install PySpark (Local System or Google Colab)
• Perform ETL on a Large Dataset using PySpark

Module 7: Advanced Spark (MLlib & GraphX)

• Spark MLlib for Machine Learning
• Clustering, Classification, Regression in Spark
• Graph Processing using GraphX

LAB 7

• Use Spark MLlib for Sentiment Analysis (Twitter/Kaggle Dataset)
• Run KMeans Clustering on a Customer Dataset

Module 8: Streaming & Real-Time Analytics

• Real-Time Data Processing Concepts
• Apache Kafka & Spark Streaming
• Use Cases: Fraud Detection, IoT Analytics

LAB 8

• Install Kafka and Simulate Real-Time Event Streaming
• Process Data Stream using PySpark Streaming

Learning Outcome

Enhance skills in processing and managing big data, implement real-time data processing, and build data integration pipelines while performing intermediate data analysis.

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Course Overview

Course Description:Master real-time analytics, machine learning integration, and big data pipelines. Optimize systems for speed, scale, and actionable business intelligence.

Topics Covered:

• Scalable data architecture and advanced Hadoop ecosystem components
• In-depth data analytics and machine learning with Spark MLlib
• Advanced real-time processing and event streaming (e.g., Kafka)
• Data governance, security, and compliance
• Optimization and performance tuning of big data systems

Syllabus

Module 1: Introduction to Big Data & Analytics

• What is Big Data? 5 Vs (Volume, Velocity, Variety, Veracity, Value)
• Big Data Ecosystem & Career Scope
• Traditional Databases vs Big Data Systems

LAB 1

• Explore Kaggle Datasets
• Perform Exploratory Data Analysis using Python (Pandas + Matplotlib)

Module 2: Data Collection & Ingestion

• Data Sources (Logs, IoT, Social Media, Sensors)
• Batch vs Streaming Data Ingestion
• ETL Concepts (Extract, Transform, Load)

LAB 2

• Use Python Requests + APIs to Collect Data
• Ingest Streaming Data using Kafka Local Setup

Module 3: Hadoop Ecosystem Basics

• HDFS (Hadoop Distributed File System)
• MapReduce Programming Model
• Hadoop Ecosystem (Hive, Pig, HBase, Sqoop)

LAB 3

• Install Hadoop Single-Node Cluster (Local VM or Docker)
• Run HDFS File Operations (Upload, List, Read, Delete)
• Execute a Simple MapReduce Word Count Job

Module 4: Data Warehousing & Hive

• Introduction to Hive & HQL (Hive Query Language)
• Data Warehousing Concepts
• Partitioning & Bucketing

LAB 4

• Install Apache Hive
• Run SQL Queries on Hive Tables (SELECT, GROUP BY, JOIN)

Module 5: Data Processing with Apache Pig & HBase

• Pig Latin Basics
• NoSQL Databases: HBase Overview
• HBase Data Model & CRUD Operations

LAB 5

• Run Pig Latin Scripts for Log Processing
• Create an HBase Table & Insert/Query Data

Module 6: Apache Spark for Big Data

• Spark Architecture & RDDs
• DataFrames & Datasets
• Spark SQL Basics

LAB 6

• Install PySpark (Local System or Google Colab)
• Perform ETL on a Large Dataset using PySpark

Module 7: Advanced Spark (MLlib & GraphX)

• Spark MLlib for Machine Learning
• Clustering, Classification, Regression in Spark
• Graph Processing using GraphX

LAB 7

• Use Spark MLlib for Sentiment Analysis (Twitter/Kaggle Dataset)
• Run KMeans Clustering on a Customer Dataset

Module 8: Streaming & Real-Time Analytics

• Real-Time Data Processing Concepts
• Apache Kafka & Spark Streaming
• Use Cases: Fraud Detection, IoT Analytics

LAB 8

• Install Kafka and Simulate Real-Time Event Streaming
• Process Stream using PySpark Streaming

Module 9: NoSQL Databases for Big Data

• Key-Value Stores (Redis)
• Document Stores (MongoDB)
• Columnar Stores (Cassandra)

LAB 9

• Install MongoDB Community Edition
• Insert & Query Large JSON Dataset

Module 10: Big Data Visualization & BI Tools

• Visualization Frameworks (Matplotlib, Seaborn, Plotly)
• Dashboards: Tableau Public, Power BI Desktop
• Connecting Visualization Tools with Big Data

LAB 10

• Create Interactive Dashboards with Plotly/Dash
• Use Tableau Public to Visualize Big Data CSV

Module 11: Big Data on Cloud

• Big Data Services on AWS, GCP, Azure
• Google BigQuery Basics
• Cloud Storage Integration

LAB 11

• Run Queries on Google BigQuery Free Tier
• Store & Process Data using AWS S3 + Athena (Free Tier)

Module 12: Big Data Capstone Project

• End-to-End Big Data Pipeline Design
• Combining Hadoop, Spark, NoSQL, Visualization
• Performance Tuning & Optimization

LAB 12

• Choose a Real-World Dataset (Social Media, IoT, E-Commerce)
• Perform: Data Ingestion → Processing (Spark) → Storage (MongoDB/HDFS) → Visualization (Dash/Tableau)
• Deploy Project on GitHub/Google Cloud Free Tier

Learning Outcome

Master scalable data architecture, apply advanced analytics and machine learning, manage real-time processing and data governance, and optimize big data systems for performance.

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