What you will learn

• Hadoop
• MapReduce
• Spark
• HDFS
• Data pipelines

Course content (36 lessons)

1. Unit I: Overview — This unit provides a foundational understanding of Big Data, its core characteristics (The 5 Vs), and its transformative impact across various industries like Finance, Healthcare,…
2. 1.1 Deep Dive: What and Why of Big Data — Introduction to the Big Data Era In the modern digital landscape, data is often referred to as the "new oil." However, unlike oil, data is inexhaustible and its value increases…
3. 1.2 Data Types and Examples — Understanding Unstructured Data Traditional databases (SQL) are designed for Structured Data —data that fits neatly into rows and columns (like an Excel sheet). However, the vast…
4. 1.3 Big Data in Marketing & Web Analytics — The Transformation of Marketing Before Big Data, marketing was often a "spray and pray" approach—running expensive TV ads and hoping some viewers would buy. Big Data has turned…
5. 1.4 Big Data in Finance & Risk Management — The Financial Frontier In the financial sector, Big Data is used to manage risks that were previously invisible. 1. Fraud Detection and Prevention Traditional fraud detection used…
6. 1.5 Big Data in Medicine & Advertising — 1.5.1 Big Data in Medicine: Saving Lives with Data Healthcare is moving from a "one-size-fits-all" approach to Precision Medicine . 1. Genomic Analytics Mapping the human genome…
7. 1.6 Big Data Technologies: The Ecosystem — The Infrastructure of Big Data You cannot process Big Data using a single computer. You need a Cluster —a collection of interconnected computers working together. Apache Hadoop:…
8. 1.7 Emerging Trends and Advanced Analytics — 1.7.1 Cloud and Big Data The cloud has democratized Big Data. Previously, only giant corporations could afford a Hadoop cluster. Now, a startup can rent a 1,000-node cluster for…
9. Unit II: Overview — In this unit, we dive into the diverse world of Data Models beyond the traditional Relational database. You will learn about NoSQL architectures, including Key-Value, Document,…
10. 2.1 Introduction to NoSQL & Aggregate Data Models — 2.1.1 The Rise of NoSQL For decades, Relational Database Management Systems (RDBMS) like MySQL and Oracle were the only choice for data storage. However, the Big Data explosion…
11. 2.2 Key-Value and Document Data Models — 2.2.1 Key-Value Databases Key-Value stores are the simplest NoSQL data models. Every item is stored as an attribute name (key) together with its value. Key : A unique identifier…
12. 2.3 Graph and Schemaless Databases — 2.3.1 Graph Databases Graph Databases focus on the relationships (edges) between data points (nodes). In a relational DB, modeling complex relationships (like "Friends of…
13. 2.4 Distribution Models: Scaling Big Data — 2.4.1 Sharding: Horizontal Partitioning Sharding is the process of splitting a large dataset across multiple database servers (shards). How it works : A "Sharding Key" decides…
14. 2.5 Consistency and Version Stamps — 2.5.1 The CAP Theorem Proposed by Eric Brewer, the CAP Theorem states that a distributed system can only provide two of the three following guarantees at once: 1. Consistency :…
15. 2.6 The Map-Reduce Computational Model — 2.6.1 The Philosophy of Map-Reduce Map-Reduce is a programming model designed to process vast amounts of data in parallel by splitting the task across a cluster. 2.6.2 The Three…
16. Unit III: Overview — Unit III focuses on the practical basics of Hadoop. We explore HDFS in depth—its master-slave architecture, data flow, and integrity mechanisms. You will also learn about the…
17. 3.1 Data Format & Analyzing Data with Hadoop — 3.1.1 The Challenge of Diverse Data Formats In the Big Data world, data arrives in various formats—from structured logs to unstructured social media feeds. Hadoop must be able to…
18. 3.2 Hadoop Streaming and Pipes — 3.2.1 Hadoop Streaming While Hadoop is written in Java, Hadoop Streaming allows you to write MapReduce programs in any language that can read from standard input (stdin) and write…
19. 3.3 Design of HDFS & Core Concepts — 3.3.1 The HDFS Design Philosophy The Hadoop Distributed File System (HDFS) is designed to store very large files across machines in a large cluster. It prioritizes Throughput over…
20. 3.4 Data Flow and the Java Interface — 3.4.1 The HDFS Java Interface Hadoop is written in Java, and its API is the most powerful way to interact with HDFS. The core class is org.apache.hadoop.fs.FileSystem . Key…
21. 3.5 Hadoop I/O: Integrity and Compression — 3.5.1 Data Integrity In a system with thousands of disks, corruption is inevitable. Hadoop uses Checksums to ensure data hasn't been corrupted. Checksum Storage : For every 512…
22. 3.6 Serialization, Avro & Data Structures — 3.6.1 What is Serialization? Serialization is the process of turning an object in memory (like a Java object) into a binary format that can be sent over the network or saved to…
23. Unit IV: Overview — This unit covers the core mechanics of MapReduce. We examine the anatomy of a job run, the transition from classic MR to YARN, and the critical "Shuffle and Sort" phase. We also…
24. 4.1 MapReduce Development: Workflows & Testing — 4.1.1 MapReduce Workflows Most real-world Big Data problems cannot be solved with a single MapReduce job. Instead, we use a Workflow —a series of jobs where the output of one job…
25. 4.2 Anatomy of a MapReduce Job Run — 4.2.1 The Classic MapReduce (MR1) Architecture In the early versions of Hadoop (0.x, 1.x), the job run was managed by two main daemons: JobTracker (Master) : Coordinates the…
26. 4.3 Failures, Scheduling & Task Execution — 4.3.1 Handling Failures: The Resilience of Hadoop Hadoop is built on the principle that "Failure is the norm." 1. Task Failure - If a Mapper or Reducer crashes, the NM reports the…
27. 4.4 The Heart of MapReduce: Shuffle and Sort — 4.4.1 Understanding the Shuffle and Sort The Shuffle and Sort is the stage where the output of the Mappers is moved to the Reducers. It is often the most expensive part of a job…
28. 4.5 MapReduce Types and Input Formats — 4.5.1 The Types: Key-Value Pairs In Hadoop, every Mapper and Reducer must follow a specific signature: - Mapper : (K1, V1) - list(K2, V2) - Reducer : (K2, list(V2)) - list(K3, V3)…
29. 4.6 Output Formats & Advanced Job Optimization — 4.6.1 Output Formats: How Hadoop Writes Data The OutputFormat defines how the final key-value pairs from the Reducers are written to HDFS. 1. TextOutputFormat : The default.…
30. End Term Important Questions — End Term Important Questions — PYQ Analysis Based on an analysis of the last three end-term papers (Dec 2021, Dec 2024, Dec 2025). Questions marked ★ Must Do have appeared in all…
31. PYQ: Important Questions — Solved
32. Top 30 Definitions & 50 Viva Questions — Top 30 Definitions (Must-Know for Short Questions) Term Definition :--- :--- :--- 1 Big Data Extremely large datasets unmanageable by traditional tools; characterized by the 5 V's…
33. PYQ: End Term December 2025
34. PYQ: End Term December 2024
35. PYQ: End Term December 2023
36. PYQ: End Term December 2022

Unit I: Overview

This unit provides a foundational understanding of Big Data, its core characteristics (The 5 Vs), and its transformative impact across various industries like Finance, Healthcare, and Marketing. We also explore the fundamental infrastructure that makes Big Data processing possible, specifically the Hadoop ecosystem.

1.1 Deep Dive: What and Why of Big Data

Introduction to the Big Data Era

In the modern digital landscape, data is often referred to as the "new oil." However, unlike oil, data is inexhaustible and its value increases the more it is refined and analyzed. Big Data is the term used to describe the massive volume of both structured and unstructured data that is so large it's difficult to process using traditional database and software techniques.

Formal Definition

Big Data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. It is characterized by high volume, high velocity, and high variety, requiring new forms of processing to enable enhanced decision making, insight discovery, and process optimization.

Why Big Data? The Necessity of Scale

The transition to Big Data wasn't a choice; it was an inevitable consequence of several global factors:

1. Explosive Growth of Data Sources: Every click, swipe, "like," and transaction creates a digital footprint.
2. Storage Costs: The cost of storing a gigabyte of data has plummeted from hundreds of dollars to fractions of a cent, allowing organizations to keep everything.
3. Processing Power: The rise of distributed computing (clusters of cheap commodity hardware) made it possible to process petabytes of data in minutes.
4. Strategic Value: Companies realized that "gut feeling" is no longer enough. Data-driven decisions provide a mathematical edge in competitive markets.

Key Benefits of Big Data Adoption

The Convergence of Key Trends

Big Data didn't emerge in a vacuum. It is the result of three major technological shifts converging:

• The Social Revolution: Platforms like X (Twitter), Facebook, and Instagram generate a non-stop stream of human sentiment and interaction data.
• The Mobile Revolution: Smartphones are effectively sophisticated sensor arrays (GPS, Accelerometer, Microphone) that transmit data 24/7.
• The Cloud Revolution: Cloud computing decoupled storage from compute, providing the "elasticity" needed to handle data spikes without buying new physical servers.

The 5 Vs: The DNA of Big Data

To truly understand Big Data, one must look at its core characteristics:

1. Volume: The sheer scale of data. We have moved from Megabytes to Gigabytes, then Terabytes, Petabytes, and now Exabytes.
2. Velocity: The speed at which data is generated and must be processed. Think of a stock market feed where milliseconds matter.
3. Variety: Data comes in all shapes—text, audio, video, sensor logs, GPS coordinates, and traditional database records.
4. Veracity: The "messiness" of data. This refers to the data quality and the level of trust one has in the data. In the world of Big Data, veracity is a major challenge because data is often collected from noisy, unverified sources (e.g., social media bot traffic, malfunctioning IoT sensors).

• Data Cleansing: The process of detecting and correcting (or removing) corrupt or inaccurate records.
• Trust Provenance: Tracking the origin of data to ensure it hasn't been tampered with.

1. Value: The most important V. Data is useless unless it can be turned into an insight that generates value for the organization.

• Monetization: Selling data or insights derived from it (e.g., Credit scoring models).
• Optimization: Using data to shave milliseconds off a process, which can lead to millions in savings.

1.1.2 Big Data Governance and Ethics

As data volumes grow, so does the risk. Modern Big Data professionals must understand:

• Data Privacy (GDPR/CCPA): Ensuring personal data is handled legally and ethical.
• Algorithmic Bias: Preventing models from making discriminatory decisions based on historical data.
• Data Stewardship: Clearly defining who "owns" and is responsible for data quality.

Frequently asked questions

Is the Big Data-1 course really free?

Yes. The entire Big Data-1 course on Siksha Sarovar is free to read with no account required. You can optionally sign in with Google to save your progress.

Do I get a certificate for Big Data-1?

Yes — finish the lessons and pass the quiz to earn a free, verifiable certificate you can share on LinkedIn or with recruiters.

Can I run code while learning?

Yes. The built-in online compiler runs C, C++, Python, Java, PHP, JavaScript, C# and SQL directly in your browser — no installation needed.