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Apache Hadoop syllabus : Latest Updated Syllabus for syllabus

Updated on Nov 13, 2024 05:14 IST

Apache Hadoop is an open-source framework that helps process large datasets in a distributed computing environment. It is built in Java and enables data processing across multiple machines in a cluster. It breaks down the task into smaller, parallel and manageable workloads. This parallel processing speeds up big data analysis, making it more efficient.

Hadoop can handle both structured data (like databases) and unstructured data (like text or images). It is highly scalable, meaning it can start on a single server and expand to thousands of machines if required.

Table of Contents

Apache Hadoop Syllabus
- 1.1 1. Introduction to Big Data & Hadoop
- 1.2 2. Hadoop Distributed File System (HDFS)
- 1.3 3. MapReduce Basics & Optimization
- 1.4 5. Apache HBase
- 1.5 6. Apache Hive
- 1.6 7. Apache Pig
- 1.7 8. Apache Sqoop
- 1.8 9. Apache Flume
- 1.9 10. Apache HCatalog
- 1.10 11. Apache Oozie
- 1.11 12. Apache ZooKeeper
- 1.12 13. Apache Phoenix
- 1.13 14. Introduction to Spark
- 1.14 15. Spark Advanced Concepts
- 1.15 16. Spark Integration with Hadoop

Apache Hadoop Syllabus

You can consider taking up Hadoop courses to understand how to store and process large amounts of data with Hadoop and Big Data technologies. These courses cover everything from the basics to advanced topics, helping you understand how to work with Big Data.

1. Introduction to Big Data & Hadoop

Usually, any Apache Hadoop course starts by providing an overview of Big Data and explaining why special tools are required for storage and processing. You will also get an introduction to Hadoop, a powerful open-source framework for managing and analyzing large datasets across distributed systems.

Main Topic	Description	Subtopics
Introduction to Big Data	Overview of Big Data concepts, challenges, and opportunities.	What is Big Data? Evolution of Big Data Benefits of Big Data Big Data characteristics Big Data opportunities Big Data challenges Operational vs Analytical Big Data Need for Big Data Analytics
Introduction to Hadoop	Introduction to the Hadoop framework and its components.	What is Hadoop? Hadoop components overview History and evolution of Hadoop Industries using Hadoop Fundamental concepts of Hadoop YARN and MapReduce Hadoop Cluster Planning Hadoop Ecosystem Hadoop 2.x core components Hadoop Storage: HDFS Hadoop Processing: MapReduce Framework Hadoop Different Distributions

2. Hadoop Distributed File System (HDFS)

HDFS is Hadoop's storage system that distributes large data across multiple nodes in a cluster. In this section, you will learn how it splits data into blocks, replicates them for fault tolerance, and ensures reliable data storage.

Main Topic	Description	Subtopics
Hadoop Distributed File System (HDFS)	Explanation of Hadoop's storage layer.	HDFS Design and concepts Blocks, NameNodes, and DataNodes Data Locality in HDFS HDFS architecture and its advantages Features of Hadoop Distributed File Entering data into HDFS
HDFS: High Availability & Scaling	Discusses fault tolerance, high availability, and scaling HDFS.	HDFS Federation HDFS High-Availability (HA) Adding and decommissioning DataNodes FSCK utility (Block Report)

3. MapReduce Basics & Optimization

MapReduce is the core processing model in Hadoop, where data is split into smaller chunks for parallel processing. This section covers how to optimize MapReduce jobs for better performance and efficient data processing. In the later part of the module, you will learn about the advanced MapReduce concepts like custom input formats, distributed caching, and optimizing performance for handling larger datasets in a more scalable way.

Main Topic	Description	Subtopics
MapReduce Basics	Fundamentals of the MapReduce processing model.	Introduction to MapReduce MapReduce Use Cases Why MapReduce Map and Reduce phases Hadoop 2.x MapReduce Architecture Hadoop 2.x MapReduce Components YARN MR Application Execution Flow YARN Workflow Anatomy of MapReduce Program Demo on MapReduce Input Splits Relation between Input Splits and HDFS Blocks Job Completion and Failures
MapReduce Optimization	Discusses optimization techniques in MapReduce.	MapReduce: Combiner & Partitioner Speculative execution JVM reuse Combiner usage Partitioning and shuffling Optimizing MapReduce performance
MapReduce Advanced Concepts	Advanced topics in MapReduce programming	Counters Distributed Cache MRunit Reduce Join Custom Input Format Custom data types in MapReduce Hadoop Streaming (Python, Ruby, R) Sequence Input Format XML file Parsing using MapReduce
YARN: Resource Management	Overview of YARN (Yet Another Resource Negotiator).	YARN architecture Resource Management in YARN YARN vs. Hadoop MapReduce Job Scheduling and Execution

5. Apache HBase

HBase is a NoSQL database built on top of Hadoop designed for real-time access to large datasets. In this part, you will explore its architecture, use cases, and how it stores and retrieves data efficiently at scale.

Main Topic	Description	Subtopics
Apache HBase	Introduction to HBase, a NoSQL database.	HBase architecture HBase Data Model Master and Region Servers HBase DDL and DML operations HBase v/s RDBMS HBase Components HBase Architecture Run Modes & Configuration HBase Cluster Deployment
HBase Advanced Topics	Advanced features and operations in HBase.	HBase Filters Bulk Loading data into HBase Sharding and Block Cache HBase Counters and Replication HBase performance tuning

6. Apache Hive

Hive is a data warehouse system for Hadoop that allows you to query and manage large datasets using SQL-like language (HiveQL). In this section, you will learn how Hive simplifies data analysis tasks with its structured query interface on top of HDFS.

Main Topic	Description	Subtopics
Apache Hive	Introduction to Hive, a data warehouse built on top of Hadoop.	What is Hive? Features of Hive The Hive Architecture Components of Hive Installation & configuration Primitive types Complex types Built-in functions Hive UDFs Views & Indexes Hive Data Models Hive vs Pig Co-groups Importing data Hive DDL statements Hive Query Language Data types & Operators Type conversions Joins Sorting & controlling data flow Local vs MapReduce mode Partitions Buckets
Hive Integration	Integration of Hive with other Hadoop components.	Accessing HBase data using Hive Integrating with HDFS and MapReduce Using Hive for log analysis

7. Apache Pig

Pig is a high-level platform for data processing using its own language, Pig Latin. You will learn how to write Pig scripts for data transformations and easily perform complex data analysis tasks without the need to write low-level MapReduce code.

Main Topic	Description	Subtopics
Apache Pig	Introduction to Pig, a high-level platform for data processing.	Pig Latin and its syntax Schema on Read Data Loading, Storing, and Processing Debugging Pig scripts User Defined Functions (UDFs) What is Apache Pig? Why Apache Pig? Pig features Where should Pig be used Where not to use Pig The Pig Architecture Pig components Pig v/s MapReduce Pig v/s SQL Pig v/s Hive Pig Installation Pig Execution Modes & Mechanisms Grunt Shell Commands Pig Latin – Data Model Pig Latin Statements Pig data types Pig Latin operators CaseSensitivity Grouping & Co Grouping in Pig Latin Sorting & Filtering Joins in Pig Latin Built-in Function Writing UDFs Macros in Pig
Pig Advanced Topics	Advanced Pig features and techniques.	Types of Joins in Pig Replicated Join Multi-query execution in Pig Piggy Bank (library of reusable UDFs) Using Pig with HBase and JSON

8. Apache Sqoop

Apache Sqoop is a command-line interface application that is used to transfer bulk data between Hadoop and relational databases efficiently. In this module, you will learn how to import and export data to and from SQL-based systems, simplifying data movement in and out of Hadoop.

Main Topic	Description	Subtopics
Apache Sqoop	Introduction to Sqoop for importing/exporting data between Hadoop and RDBMS.	Fundamentals of Sqoop Scoop installation Importing data (full, incremental) from MySQL to Hadoop HDFS Importing data to Hive Importing data to HBase Controlling import process Working of Sqoop Understanding connectors Selective imports Exporting data to MySQL from Hadoop Exporting data to RDBMS, Hive, and HBase Free-form queries and file formats

9. Apache Flume

Flume is a tool for ingesting large amounts of log and event data into Hadoop. You will explore how to set up and configure Flume agents to collect and transfer streaming data from various sources to HDFS.

Main Topic	Description	Subtopics
Apache Flume	Introduction to Flume for real-time data ingestion.	Flume Architecture Flume Agents, Sources, Sinks Collecting logs and events from different sources (e.g., Twitter) Data flow in Flume Flume features Flume Event

10. Apache HCatalog

HCatalog provides a shared metadata layer for accessing data across multiple Hadoop tools like Hive, Pig, and MapReduce. In this module, you will learn how to use HCatalog to manage and share data schemas across the Hadoop ecosystem.

Main Topic	Description	Subtopics
Apache HCatalog	Introduction to HCatalog for shared metadata management in Hadoop.	HCatalog - Introduction HCatalog Architecture HCatalog - Installation HCatalog - CLI Commands HCatalog - Create Table HCatalog - Alter Table HCatalog - Show Tables HCatalog - Show Partitions HCatalog - Indexes HCatalog APIS Integrating HCatalog with Pig, Hive, and MapReduce HCatalog for Data Interchange HCatalog - Reader Writer HCatalog - Input Output Format HCatalog - Loader and Storer

11. Apache Oozie

Oozie is a workflow scheduler used to manage Hadoop jobs. In this section, you will learn how to define, schedule, and manage complex workflows involving multiple Hadoop tasks like Hive, Pig, and MapReduce jobs.

Main Topic	Description	Subtopics
Apache Oozie	Introduction to Oozie for workflow management in Hadoop.	Oozie architecture and components Scheduling jobs (MapReduce, Hive, Sqoop) Coordinators and Bundles in Oozie Oozie Bundle system CLI and extensions Overview of Hue

12. Apache ZooKeeper

ZooKeeper is a distributed coordination service that helps manage and synchronize services in Hadoop-based applications. You will learn its role in ensuring high availability, leader election, and configuration management in distributed environments.

Main Topic	Description	Subtopics
Apache ZooKeeper	Overview of ZooKeeper for distributed coordination.	ZooKeeper architecture Leader Election algorithm Use cases for ZooKeeper in Hadoop ecosystems ZooKeeper Data Model Zookeeper Service

13. Apache Phoenix

Phoenix is a SQL layer on top of HBase that provides SQL support for real-time read/write operations. Learn how to run SQL queries on HBase tables and manage HBase data using traditional SQL syntax.

Main Topic

Description

Subtopics

Apache Phoenix

Introduction to Phoenix for SQL on HBase.

Setting up Phoenix with HBase

Writing SQL queries on HBase

Performance optimization using Phoenix

14. Introduction to Spark

Apache Spark is an advanced, in-memory data processing engine for big data analytics. This module covers Spark's ability to process batch and real-time data much faster than traditional MapReduce, making it an essential tool in big data ecosystems.

Main Topic

Description

Subtopics

Introduction to Spark

Overview of Apache Spark, a fast, in-memory data processing engine.

Spark overview and architecture

Resilient Distributed Datasets (RDDs)

Spark transformations and actions

Spark deployment models

15. Spark Advanced Concepts

As the name suggests, this section dives into advanced Spark topics such as RDD (Resilient Distributed Datasets), transformations, actions, and Spark Streaming for real-time processing. You will also learn performance tuning and optimization techniques to handle large-scale data efficiently.

Main Topic

Description

Subtopics

Spark Advanced Topics

Advanced topics in Spark programming.

RDD persistence and storage levels

Shared variables, broadcast variables

Accumulators and Spark Streaming

181949

16. Spark Integration with Hadoop

Spark seamlessly integrates with Hadoop to use HDFS for storage and YARN for resource management. In this module, you will learn how to run Spark applications on a Hadoop cluster, enabling faster processing of large datasets.

Main Topic

Description

Subtopics

Spark Integration with Hadoop

Integration of Spark with Hadoop ecosystem.

Running Spark jobs on YARN

Migrating from Hadoop MapReduce to Spark

Using Spark with HDFS and HBase

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1 Course

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4 Courses

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ExcelRBangalore

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1 Course

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SimplilearnAll over India

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9 Courses

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Total Tuition Fees

₹0 - 23.63 K

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Course Rating

4.719

Ranked

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National Institute of Electronics and Information Technology, ChandigarhChandigarh

Admission '25 Placement

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3 Courses

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8 Courses

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4.413

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UG Courses

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PG Courses

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Popular Exams

Following are the top exams for Apache Hadoop. Students interested in pursuing a career on Apache Hadoop, generally take these important exams.You can also download the exam guide to get more insights.

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