Master Hadoop: Complete Certification Course in Big Data and Distributed Processing

In today’s data-driven world, organizations are continuously challenged to handle and process
massive volumes of structured and unstructured data. Apache Hadoop, an open-source framework,
has emerged as the go-to solution for big data storage and processing. Hadoop allows businesses to
store data across distributed systems and run powerful data analytics, transforming raw data into
valuable insights.

ENCODE-IT’s Comprehensive Hadoop Certification Course is designed to provide you with a deep
understanding of Hadoop and its ecosystem. From its core components like HDFS (Hadoop
Distributed File System) and MapReduce to advanced tools like Hive, Pig, and HBase, this course will
equip you with the skills to manage and analyze large datasets effectively. With real-world projects
and hands-on experience, you’ll be prepared to become a Hadoop professional capable of
implementing end-to-end big data solutions.
Whether you’re a data scientist, software engineer, or IT professional looking to delve into the world
of big data, this course will help you unlock the power of Hadoop for your organization’s data
processing needs.

Salary Scale in India
Hadoop skills are highly in demand as companies increasingly adopt big data solutions. Entry-level
professionals such as Hadoop Developers or Big Data Engineers can earn between ₹6,00,000 and
₹12,00,000 per year. Experienced professionals in roles such as Hadoop Architect or Big Data
Analyst can earn ₹15,00,000 to ₹25,00,000 annually. As the big data field grows, skilled professionals
in Hadoop are among the most sought-after by companies across sectors such as finance,
healthcare, retail, and tech.

Placement Assistance & Certification
On successful completion of the Hadoop Certification Course, you will receive an official certification
from ENCODE-IT, validating your expertise in Hadoop and big data technologies. Additionally,
ENCODE-IT offers placement assistance, connecting you with leading organizations that are actively
looking for Hadoop professionals. This ensures that you are not only equipped with the technical
knowledge but also have the support to launch your career in the big data field.

Course Curriculum

1. Introduction to Big Data and Hadoop
 Understanding Big Data: Characteristics and Challenges
 Overview of Hadoop and Its Role in Big Data Processing
 The Hadoop Ecosystem: Key Components and Tools
 Benefits of Hadoop for Distributed Data Storage and Processing
 Setting Up Hadoop: Installation and Configuration
 Hadoop Cluster Architecture and Components: HDFS, MapReduce, YARN
 Introduction to Hadoop in the Cloud (AWS, Azure, Google Cloud)

2. Hadoop Distributed File System (HDFS)

 Overview of HDFS: Architecture, Components, and Data Flow
 Understanding Data Blocks, Replication, and Fault Tolerance in HDFS
 Interacting with HDFS: Commands and Operations
 HDFS Administration: Managing Files and Directories
 Configuring HDFS for Scalability and High Availability
 Optimizing HDFS for Performance and Data Processing

3. MapReduce Programming Model
 Introduction to MapReduce: Key Concepts and Workflow
 Writing Your First MapReduce Program
 Input, Mapper, Reducer, and Output Formats in MapReduce
 Understanding Job Configuration and Execution in MapReduce
 Optimizing MapReduce Jobs for Performance and Resource Management
 Debugging and Troubleshooting MapReduce Jobs
 Working with Advanced MapReduce Features (Combiner, Partitioner)

4. Apache Hive for Data Warehousing
 Introduction to Apache Hive: Data Warehousing and SQL-like Queries
 Setting Up and Configuring Hive in a Hadoop Cluster
 Creating Databases, Tables, and Schemas in Hive
 Querying Data Using HiveQL (Hive Query Language)
 Working with Complex Data Types: Arrays, Maps, Structs
 Partitioning and Bucketing in Hive for Performance Optimization
 Integrating Hive with HDFS for Data Storage and Retrieval
 Advanced Hive Features: User-Defined Functions (UDFs) and Views

5. Apache Pig for Data Flow and Scripting
 Introduction to Apache Pig: Data Flow and ETL Processing
 Writing Pig Scripts for Data Transformation and Analysis
 Understanding the Pig Latin Language for Data Manipulation
 Loading and Storing Data with Pig: Working with HDFS
 Optimizing Pig Scripts for Performance
 Advanced Pig Functions: Joins, Grouping, and Filtering
 Integrating Pig with Hive and HBase for Data Processing

6. Managing Big Data with Apache HBase
 Introduction to HBase: NoSQL Database for Hadoop
 Setting Up and Configuring HBase for Distributed Storage
 HBase Architecture: Region Servers, MemStore, WAL, and HFiles
 CRUD Operations in HBase: Inserting, Updating, and Deleting Data
 Working with HBase Data Model: Row Keys, Columns, and Column Families
 Integrating HBase with Hive and Pig for Enhanced Analytics
 Advanced HBase Features: Bulk Loading, Filters, and Scanners

7. Data Processing with Apache Spark on Hadoop
 Introduction to Apache Spark: In-Memory Processing for Big Data
 Setting Up Spark with Hadoop: Integration and Configuration
 Spark RDDs (Resilient Distributed Datasets) and DataFrames
 Spark SQL: Querying Big Data with SQL in Spark
 Spark Streaming: Real-Time Data Processing
 Machine Learning with MLlib in Spark
 Using GraphX for Graph Processing and Analytics
 Optimizing Spark Jobs for Performance and Efficiency

8. Hadoop Ecosystem Tools for Data Processing
 Introduction to Other Hadoop Ecosystem Tools: Flume, Sqoop, Oozie
 Data Ingestion with Apache Flume: Collecting Streaming Data
 Importing and Exporting Data with Apache Sqoop
 Scheduling and Managing Data Workflows with Apache Oozie
 Real-Time Data Processing with Apache Storm
 Data Integration and Processing with Apache Kafka
 Monitoring and Managing Hadoop with Apache Ambari and Cloudera Manager

9. Hadoop Security and Administration
 Securing Hadoop Clusters with Kerberos Authentication
 Implementing Data Encryption and Authorization in HDFS and MapReduce
 Managing User Permissions with Hadoop Access Control Lists (ACLs)
 Auditing and Logging in Hadoop for Compliance and Security
 Hadoop Cluster Management: Adding Nodes, Resource Management, and Monitoring

 Managing and Scaling Hadoop Clusters in Production
 Troubleshooting and Optimizing Hadoop Performance

10. Real-World Projects and Case Studies
 Case Study: Implementing a Data Warehouse with Hive and Hadoop
 Building an End-to-End Data Pipeline with Pig, Hive, and HBase
 Real-World Data Processing Project Using Spark on Hadoop
 Hadoop for Business Intelligence: Analyzing Large Datasets for Insights
 Integrating Hadoop with Cloud Platforms for Scalable Solutions
 Project: Migrating Legacy Systems to Hadoop Ecosystem

11. Final Project and Certification Exam
 Final Project: Design and Implement a Big Data Solution Using Hadoop
 Handling Data Ingestion, Transformation, Storage, and Analytics
 Optimizing Performance and Ensuring Scalability in a Real-World Scenario
 Final Exam: Comprehensive Assessment of Hadoop Skills
 Certification of Completion from ENCODE-IT and Job Placement Assistance

Key Features
 Tools & Platforms: Hadoop, HDFS, MapReduce, Hive, Pig, HBase, Spark, Flume, Sqoop, Oozie
 Real-World Projects: Hands-on experience with data processing, storage, and analytics using
Hadoop
 Certification & Placement Support: Hadoop certification and job placement assistance
 Expert Instructors: Learn from industry professionals with expertise in Hadoop and big data
technologies
 Career Advancement: Build in-demand skills for big data analytics, cloud computing, and
data engineering

Why Choose ENCODE-IT for Hadoop Certification?
ENCODE-IT’s Comprehensive Hadoop Certification Course provides a thorough understanding of
Hadoop and its ecosystem, enabling you to process, manage, and analyze massive datasets. With
practical experience in deploying Hadoop solutions and solving real-world challenges, this course will
prepare you to excel in the big data field. Enroll today to unlock your potential and advance your
career in the rapidly growing field of data engineering and analytics!