Master Big Data Analytics with ENCODE-IT’s Big Data and Hadoop Course

Big Data is transforming the way organizations analyze and utilize data to make strategic decisions.
With massive datasets being generated every day, the need for professionals skilled in Big Data
technologies is at an all-time high. ENCODE-IT’s Big Data and Hadoop course is designed to provide
you with a solid foundation in the Hadoop ecosystem and teach you how to process and analyze Big
Data. This course covers the fundamentals of Hadoop, the Hadoop Distributed File System (HDFS),
MapReduce programming, and advanced Big Data tools like Apache Hive, Apache Pig, and Spark.

Whether you're a beginner looking to start a career in Big Data or an experienced professional
wanting to expand your skills, this course will equip you with the expertise to handle and analyze
large datasets, ensuring you're ready to tackle real-world challenges in the Big Data space.

Salary Scale in India:
The demand for Big Data professionals, especially those proficient in Hadoop, is skyrocketing. In
India, professionals with Big Data and Hadoop skills can expect lucrative salaries across various
levels:
 Entry-Level Big Data Developer: ₹5 Lakhs to ₹8 Lakhs per annum
 Mid-Level Big Data Engineer: ₹8 Lakhs to ₹14 Lakhs per annum
 Senior Big Data Architect: ₹15 Lakhs to ₹30 Lakhs+ per annum
As businesses continue to rely on Big Data for decision-making, skilled Hadoop professionals are
well-compensated, making this a promising and rewarding career path.

ENCODE-IT’s Placement Assistance and Certification:
Upon completion of the Big Data and Hadoop course, you will receive an ENCODE-IT Certification
that will add value to your resume. Additionally, our Placement Assistance team will help you
connect with top employers in the Big Data field, providing you with guidance to secure your desired
job role in the industry.

Course Curriculum

1. Introduction to Big Data
 What is Big Data? Characteristics and Importance
 Big Data vs. Traditional Data Processing
 Big Data Technologies: Hadoop, Spark, NoSQL Databases
 Overview of the Big Data Ecosystem
 Understanding Distributed Computing

2. Hadoop Fundamentals
 Introduction to Hadoop: Key Concepts and Architecture

 Hadoop Distributed File System (HDFS)
 HDFS Cluster Setup and Configuration
 Data Storage and File Management in HDFS
 Fault Tolerance and Data Replication in Hadoop

3. MapReduce Programming
 Introduction to MapReduce and Its Role in Hadoop
 Understanding MapReduce Workflow: Mapper, Reducer, Driver
 Writing MapReduce Programs in Java
 Running MapReduce Jobs on Hadoop Cluster
 Debugging and Optimizing MapReduce Jobs

4. Apache Hive
 Introduction to Hive: A SQL-Like Interface for Hadoop
 Hive Architecture: Metastore, Query Processor, Driver
 Writing and Executing Hive Queries
 Creating and Managing Tables, Partitions, and Buckets
 Integrating Hive with Hadoop Ecosystem

5. Apache Pig
 Introduction to Apache Pig and Pig Latin
 Writing and Running Pig Scripts
 Data Types and Functions in Pig
 Performing ETL Operations with Pig
 Optimizing Pig Queries for Performance

6. Data Ingestion with Apache Sqoop and Flume
 Introduction to Data Ingestion in Hadoop
 Using Apache Sqoop for Data Import/Export from RDBMS
 Real-Time Data Ingestion with Apache Flume
 Integrating Flume with Hadoop for Streaming Data

7. Apache Spark
 Introduction to Apache Spark: In-Memory Data Processing
 Spark Architecture: Driver, Executors, RDDs
 Working with RDDs and DataFrames in Spark

 Spark SQL: Querying Structured Data
 Real-Time Processing with Spark Streaming
 Machine Learning with MLlib in Spark

8. NoSQL Databases in Big Data
 Introduction to NoSQL Databases
 Key-Value Stores, Column-Family Stores, Document Stores, and Graph Databases
 Using HBase: Hadoop’s NoSQL Database for Real-Time Data
 Integrating NoSQL Databases with Hadoop Ecosystem
 Use Cases and Applications of NoSQL Databases

9. Hadoop Ecosystem Tools
 Introduction to Hadoop Ecosystem: Zookeeper, Oozie, and Kafka
 Using Zookeeper for Distributed Coordination
 Workflow Management with Oozie
 Real-Time Streaming with Apache Kafka
 Integrating Hadoop Ecosystem Tools for End-to-End Data Processing

10. Data Security and Governance in Hadoop
 Introduction to Big Data Security Challenges
 Securing Hadoop with Kerberos Authentication
 Data Encryption and Privacy in Hadoop Ecosystem
 Implementing Data Governance in Hadoop
 Using Apache Ranger and Apache Atlas for Security and Compliance

11. Big Data Analytics
 Introduction to Big Data Analytics Concepts
 Batch Processing vs. Stream Processing
 Data Visualization and Reporting with Hadoop
 Using Machine Learning Algorithms for Predictive Analytics
 Implementing Big Data Analytics for Business Insights

12. Real-World Big Data Projects
 Building a Data Warehouse with Hive for Business Intelligence
 Real-Time Data Processing with Apache Kafka and Spark Streaming
 Implementing a Batch Processing Solution with MapReduce

 Integrating Data from Different Sources with Sqoop and Flume
 Predictive Analytics using Big Data Tools

13. Final Project and Certification Exam
 Final Project: Hands-on Implementation of Big Data Solution
 Project Evaluation: Showcasing Your Skills in Hadoop and Big Data Technologies
 Final Exam: Comprehensive Test on Hadoop Concepts and Tools
 Certification of Completion from ENCODE-IT and Job Placement Assistance

Key Features of the Course
 Tools & Platforms: Hadoop, HDFS, MapReduce, Hive, Pig, Apache Spark, Sqoop, Flume,
HBase, Kafka
 Real-World Projects: Work on projects involving large datasets, real-time data streaming,
and Big Data analytics
 Certification & Placement Support: ENCODE-IT certification and job placement assistance to
help you land your desired Big Data role
 Expert Instructors: Learn from professionals with deep knowledge in Big Data and Hadoop
technologies
 Career Advancement: Enhance your skills to become an expert in Big Data, opening up
career opportunities in various industries