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Big Data Hadoop Certification Training

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Duration :  25 Days

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🌐 Big Data Hadoop Online Training

Big Data Hadoop Online Training is designed to help learners master the concepts of distributed data processing and large-scale data analytics. Hadoop is one of the most popular frameworks for managing and analyzing massive volumes of data efficiently. This course provides in-depth knowledge of Hadoop architecture, HDFS, MapReduce, YARN, Hive, Pig, Sqoop, and Spark — empowering professionals to handle Big Data Hadoop-driven decision-making and real-world analytics challenges.

Its Core Capabilities Include:

  • Big Data Hadoop Architecture & Ecosystem:
    Understand the core components of Hadoop — HDFS, YARN, and MapReduce — and how they work together for distributed Big Data Hadoop storage and processing.
  • HDFS (Big Data Hadoop Distributed File System):
    Learn how Big Data Hadoop stores large data sets across multiple nodes and provides fault tolerance.
  • MapReduce Programming:
    Develop MapReduce applications to process and analyze big data efficiently.
  • YARN (Yet Another Resource Negotiator):
    Manage computing resources and schedule data processing jobs across Hadoop clusters.
  • Hive & Pig:
    Simplify data analysis with Hive (SQL-like queries) and Pig (data flow scripts).
  • Sqoop & Flume:
    Transfer and integrate Big Data Hadoop between Hadoop and relational databases using Sqoop, and collect streaming data with Flume.
  • Apache Spark Integration:
    Gain hands-on experience with Apache Spark for real-time big data analytics and machine learning workloads.
  • Cluster Management & Security:
    Learn to configure, monitor, and secure Hadoop clusters using Ambari and Kerberos.

📍 Bonus: Certification Tracks

  • Cloudera Certified Big Data Hadoop Administrator (CCA)
  • Hortonworks Certified Big Data Hadoop Developer
  • Apache Spark and Hadoop Developer (HDPCD)
  • Big Data Hadoop Professional (Viswa Online Trainings)

Big Data Hadoop Training Course Syllabus

MODULE 1 – INTRODUCTION TO BIG DATA Hadoop
  • What is Big Data?
  • Examples of Big Data
  • Reasons for Big Data Generation
  • Why Big Data deserves your attention
  • Use cases of Big Data
  • Different options of analyzing Big Data
  • Interview Questions and Tips.
  • HADOOP SOFTWARE INSTALLATION & SETUP
  • Cloudera 5.12.0 Hadoop latest software
MODULE 2 – HDFS MODULE
  • What is Big data Hadoop
  • History of Hadoop
  • How Hadoop’s name was given
  • Advantages and drawbacks of Hadoop
  • Problems with Traditional Large-Scale Systems and the Need for Hadoop
  • Setting up single node Hadoop cluster(Pseudo mode)
  • Understanding Hadoop configuration files
  • Hadoop Components- HDFS, Map Reduce
  • Overview Of Hadoop Processes
  • Overview Of Hadoop Distributed File System
  • Understanding Hadoop Architecture 1X
  • Understanding Hadoop Architecture 2X
  • Fundamental of HDFS (Blocks, Name Node, Data Node, Secondary Name Node)
  • Rack Awareness
  • Read/Write from HDFS
  • HDFS Federation and High Availability
  • The building blocks of Hadoop
  • Types of scaling
  • Hands-On Exercise using Linux commands
  • Hands-On Exercise using HDFS commands
  • HDFS Module Interview Questions and Answers
MODULE 3 – HIVE in Big data Hadoop
  • Installing HiveHive data types
  • hive meta store tables
  • Types of Tables in Hive(Internal Table and External Table)
  • Comparison between internal table and external table
  • Loading data into hive tables
  • Writing output into local or hdfs file system
  • How to use hive commands in Linux prompt
  • How to use Hadoop commands in hive prompt
  • Partitions(Static & Dynamic)
  • Bucketing
  • Table Sampling
  • Hive functions(Numeric Functions, Aggregate Functions, Date Functions, and String Functions)
  • Indexes
  • Views
  • Sub Quires
  • Joins (Left Outer, Right Outer, Full Outer, inner join, and Cross Join)
  • Developing hive scripts
  • Parameter Substitution
  • Difference between order by & sort by
  • Difference between Cluster by & distributed by
  • Difference between view and table
  • Purpose of UNION ALL
  • DML Commands
  • Complex Data Types(Array, Map and Struct)
  • File Input formats
  • Text File
  • RC
  • ORC
  • Sequence
  • Avro
  • Parquet
  • Creating Hive UDFs
  • Hands-On Exercise
  • Hive module interview questions and answers
MODULE 4- PIG
  • Introduction to Apache Hive
  • Architecture of Hive
  • Introduction to Apache Pig
  • Building Blocks ( Bag, Tuple & Field)
  • Installing Pig
  • PIG Architecture
  • Big Data types and complex data types
  • Pig components
  • Different modes of execution of PIG
  • Types of different bags creation
  • Working with various PIG Commands covering all the functions in PIG
  • Aggregate and String functions.
  • Developing PIG scripts
  • Parameter Substitution
  • Command line arguments
  • Passing parameters through a param file
  • Joins (Left Outer, Right Outer, Full Outer, inner join, replicated and skewed join)
  • Advanced Joins (replicated join and skewed join)
  • Purpose of count and count_star
  • Group and cogroup
  • Nested queries
  • Word Count program using pig queries Working with Semi-structured data like XML and JSON
  • Complex data types using pig
  • Creating PIG UDFs
  • Hands-On Exercise
  • PIG Module Interview Questions and Answers
MODULE 5- SQOOP
  • Introduction to SQOOP
  • SQOOP Architecture
  • Import data from RDBMS to HDFS
  • Importing Data from RDBMS to HIVE
  • Importing data from RDBMS to HBASE
  • Exporting data from HDFS to RDBMS
  • Exporting data from HIVE to RDBMS
  • Handling incremental loads using sqoop to HDFS.
  • Handling incremental loads using sqoop to HIVE.
  • Handling incremental loads using sqoop to Hbase.
  • Creation of Sqoop jobs, Execution, and deletion.
  • Hands-on exercise
  • SQOOP Module Interview Questions and answers
MODULE 6- HBASE
  • Introduction to HBase
  • HBase Architecture
  • Installation of HBase
  • Exploring HBASE Master & Region server
  • Exploring Zookeeper
  • CRUD Operation of Hbase with Examples
  • HIVE integration with HBASE(HBASE-Managed hive tables)
  • Hands-on exercise on HBASE
  • Hbase Interview Questions and Answers
MODULE 7 – MAP REDUCE in Big data Hadoop
  • Understanding Map Reduce
  • Job Tracker and Task Tracker
  • Architecture of Map Reduce
  • Map Function
  • Reduce Function
  • Data Flow of Map Reduce
  • Hadoop Writable, Comparable & comparison with Java data types
  • Map Function & Reduce Function
  • How Map Reduce Works
  • Submission & Initialization of Map Reduce Job
  • Monitoring & Progress of Map Reduce Job
  • Understand Difference Between Block and Input Split
  • Role of Record Reader, Shuffler, and Sorter
  • File Input Formats(TextInputFormat, KeyValueInputFormat)
  • File output
  • Formats(TextInputFormat,KeyValueInputFormat,SequenceOutputFormat)
  • Getting Started With Eclipse IDE
  • Setting up Eclipse Development Environment
  • Creating Map Reduce Projects
  • Configuring Hadoop API on Eclipse IDE
  • Map Reduce program flow with word count
  • Combiner & Partitioner, Custom Partitioner
  • Joining Multiple datasets in Map Reduce
  • Map Reduce programs explanation
MODULE 8 – OOZIE in Big Data Hadoop
  • Oozie Introduction
  • Oozie Architecture
  • Oozie Map Reduce Jobs Execution
  • How to use the OOZIE Web-Console
  • How to use oozie through Hue
MODULE 9 – HUE in Big data Hadoop
  • Use of Hue
  • How to use HDFS module through hue
  • How to use Hive module through hue
  • How to use OOZIE module through hue
  • How to use Hbase module through hue
MODULE 10 – CLOUDERA MANAGER
  • Purpose of Cloudera manager
  • How to use Cloudera manager
  • How to start cluster and stop cluster
  • How to restart and stop only specific modules
  • How to check existing cluster nodes
  • How to remove node from the cluster
  • How to monitor health issues of the cluster
MATERIALS
  • All the modules-related materials soft copies will provide
  • Each module-related complete reference book will provide
  • Big Data Hadoop Certification Papers will provide
Big Data Hadoop Course Key Features
  • Live Instructor based training with software
  • Certification Oriented content
  • Hands-on complete Realtime training
  • Flexible schedule demo's & classes
  • Live recorded videos access
  • Study material provided
  • JOB Assistance

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Big Data Hadoop Online Training FAQ'S

What is Hadoop and why is it used?

  • Hadoop is an open-source framework used to store and process large datasets in a distributed computing environment, offering scalability and fault tolerance.

What are the main components of Hadoop?

  • The main components are HDFS (storage), YARN (resource management), and MapReduce (data processing).

What is HDFS and how does it work?

  • HDFS stores data across multiple nodes in blocks, ensuring data replication for reliability and fault tolerance.

What is the difference between Hadoop and Spark?

  • Hadoop uses batch processing through MapReduce, while Spark provides in-memory processing for faster real-time analytics.

What is a NameNode and DataNode in Hadoop

  • The NameNode manages metadata (directory tree), while DataNodes store the actual data blocks in the cluster.

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