Certificate Course on
Bigdata & Hadoop Analytics


mango db

Admission Open Now

Admission Open Now

Select Class - Mode

Short - Term
Online / Classroom

Learning Objectives
Bigdata & Hadoop + Mongo DB +Spark+Scala

The global Big Data and data engineering services market is expected to grow at a CAGR of 31.3 percent by 2025, so this is the perfect time to pursue a career in this field.

The world is getting increasingly digital, and this means big data is here to stay. The importance of big data and data analytics is going to continue growing in the coming years. Choosing a career in the field of big data and analytics might be the type of role that you have been trying to find to meet your career expectations. Professionals who are working in this field can expect an impressive salary, the median salary for a data engineer is $137,776, with more than 130K jobs in this field worldwide. As more and more companies realize the need for specialists in big data and analytics, the number of these jobs will continue to grow. A role in this domain places you on the path to an exciting, evolving career that is predicted to grow sharply into 2025 and beyond.

Bigdata & Hadoop + Mongo DB +Spark+Scala

According to Forbes, Big Data & Hadoop Market is expected to reach $99.31B by 2022. This Big Data Hadoop Certification course is designed to give you an in-depth knowledge of the Big Data framework using Hadoop and Spark, including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyse large datasets stored in the HDFS, and use Sqoop, Flume, and Kafka for data ingestion with our significant data training.

You will master Spark and its core components, learn Spark’s architecture, and use Spark cluster in real-world - Development, QA, and Production. With our Big Data Hadoop course, you will also use Spark SQL to convert RDDs to DataFrames and Load existing data into a DataFrame.

As a part of the Big Data Hadoop course, you will be required to execute real-life, industry-based projects using Integrated Lab in the domains of Human Resource, Stock Exchange, BFSI, Healthcare and Retail & Payments. This Big Data Hadoop training course will also prepare you for the Cloudera CCA175 significant Big Data certification exam.

Big Data Hadoop certification training will enable you to master the concepts of the Hadoop framework and its deployment in a cluster environment. By the end of this course, you will be able to:

What Skills you will Learn

  • Learn how to navigate the Hadoop Ecosystem and understand how to optimize its use
  • Ingest data using Sqoop, Flume, and Kafka
  • Implement partitioning, bucketing, and indexing in Hive
  • Work with RDD in Apache Spark
  • Understand and work with MongoDB
  • Process real-time streaming data
  • Prepare for Cloudera CCA175 Big Data certification exam
  • Perform DataFrame operations in Spark using SQL queries
  • Implement User-Defined Functions (UDF) and User-Defined Attribute Functions (UDAF) in Spark
Frequently asked Questions
1. Who should attend this course?

Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology in Big Data architecture. Big Data training is best suited for IT, data management, and analytics professionals looking to gain expertise in Big Data, including:

  • Software Developers and Architects
  • Analytics Professionals
  • Senior IT professionals
  • Testing and Mainframe Professionals
  • Data Management Professionals
  • Business Intelligence Professionals
  • Project Managers
  • Aspiring Data Scientists
  • Graduates looking to build a career in Big Data Analytics
2. How the Big Data training help my career?

The field of big data and analytics is a dynamic one, adapting rapidly as technology evolves over time. Those professionals who take the initiative and excel in big data and analytics are well-positioned to keep pace with changes in the technology space and fill growing job opportunities. Some trends in big data include:

  • Global Hadoop Market to Reach $84.6 Billion by 2021 – Allied Market Research
  • The global Big Data and data engineering services market is expected to grow at a CAGR of 31.3 percent by 2025
  • Big Data & Hadoop Market is expected to reach $99.31B by 2022 - Forbes
  • Hadoop Administrators in the US receive salaries of up to $123,000 –
3. What type of Jobs we can expect after this training?

Upon completion of the Big Data Hadoop training course, you will have the skills required to help you land your dream job, including:

  • IT professionals
  • Data scientists
  • Data engineers
  • Data analysts
  • Project managers
  • Program managers
4. What are the Pre-requisites for this Training?

There are no prerequisites for learning this course. However, knowledge of Core Java and SQL will be beneficial

5. Who will provide the certification?

Upon successful completion of the Big Data Hadoop certification training, you will be awarded the course completion certificate from Kigyan

6. What are my system requirements?

The tools you will need to attend training are:

  • Windows: Windows XP SP3 or higher
  • Mac: OSX 10.6 or higher
  • Intel i3 with minimum 8 GB RAM
  • Internet speed: Preferably 512 Kbps or higher for online training
  • Headset, speakers, and microphone: you will need headphones or speakers to hear instructions clearly, as well as a microphone to talk to others. You can use a headset with a built-in microphone, or separate speakers and microphone
7. What are the training modes offered for this course?

We offer this training in the following modes:

  • Live Classroom training in our Training Centre
  • Live Virtual Classroom or Online Classroom: Attend the course remotely from your desktop via video conferencing to increase productivity and reduce the time spent away from work or home
8. Any Group Discount offered in this classroom training?

Yes, we have group discount options for our training programs. Contact us using the form on the right of any page on the website or send am mail with your requirement and the student count to Our customer service representatives can provide more details.

9. What payment options are available?

Payments can be made using any of the following options. You will be emailed a receipt after the payment is made.

  • Any Credit or Debit Card
  • Bank Transfer using NEFT / RTGS
  • Direct payment in our centre through cash / cheque
  • Online payment wallet like Google Pay, PayTM….
Bigdata & Hadoop + Mongo DB +Spark+Scala
Introduction to Data Science
  • Types of Digital Data
    • Structured
    • Unstructured
    • Unstructured
  • Characteristics of Data
  • Evolution of Big Data
  • Definition of Big data
    • Volume, Velocity, variety
  • Challenges with Big data
  • Traditional BI Vs Bigdata
  • What is changing today?
Big Data Analytics
  •   What is & what is not Big data analytics
  • Classification of Analytics
    •   First School of Thought
    •   Second School of Thought
  • Top Challenges
  • Why Big data analytics Important
  • What is Data Science
    •    Business Acumen Skills
    •    Technology Expertize
    •    Mathematics and Statistics
  • Terminologies used
    •   In-memory Analysis
    •   In-database processing
    •    Symmetric multiprocessor system (SMP)
    •   Massively parallel processing
    •   Parallel vs distributed Processing
    •   Shared nothing Architecture
    •    Basically Available Soft state Eventual Consistency (BASE)
  •   The Big Date Technology Landscape
    •   NoSQL
Introduction to Hadoop
  • History of Hadoop
  • Hadoop Distributors
  • Hadoop Architecture and components
  • Understanding HDFS
    •    HDFS Demons
    •    Writing in to HDFS
    •    Reading from HDFS
    •    Replica Placement Strategy
    •    HDFS Commands
    •    Features of HDFS
  • Managing Resources with YARN
    •    YARN Infrastructure
    •    Resource Manager
    •    Additional components
    •    Application Master, Node Manager & Container
    •    Application Start-up
  • Processing DATA in Hadoop
    •    Introduction to Map Reduce
    •    Map Reduce demons
    •    How does Map Reduce Work
    •    case studies
    •    Map Execution on Distributed Environment
    •    Map Reduce Jobs
    •    Map Reduce and Associated Jobs
  • Interacting with Hadoop Ecosystem
    •    Pig
    •    Hive
    •    Sqoop
    •    HBase
Installing and Configure Hadoop
  •   Understanding Victual machine
  •   Creating Virtual server with Oracle Virtual Box
  •   Creating a Linux Virtual server
  •   Installing cloudera
  •   Installing Hottemworks
Mongo DB
  •   What is Mongo DB
  •   Data types in Mango DB
  •   JSON & BSON
  •   Terms used in Mango DB
  •   Mango DB Query Language
Apache Cassandra
  •   Introduction to Cassandra
  •   Features of Cassandra
  •   CQL Data Types
  •   CQLSH
  •   Key spaces
  •   CRUD Operations
  •   Collections
  •   Using Counters
  •   Time to Live (TTL)
  •   Alter Commands
  •   Import and Export
  •   Querying System Tables
Advanced Map Reduce
  •   Hadoop Job Work Integration
  •   Characteristics of MapReduce
  •   Real-time Uses of MapReduce
  •   Setting up env. For MapReduce Development
  •   Uploading Small Data and Big Data
  • Why Big data analytics Important
    • Requirements
    • Steps
    • Responsibilities
  •   Building a MapReduce Program
  •   Build a MapReduce application in Eclipse and Run in Hadoop
Advanced HDFS and Map Reduce
  •   Advanced HDFS
  •   HDFS Benchmarking
  •   Setting up Block size in HDFS
  •   Decommissioning a data node
  •   Advanced Map Reduce
  •   Interfaces
  •   Datatypes, input formats and Output formats in MapReduce
  •   Distributed Cache
  •   Joins in MapReduce
    •    Reduce Side Join
    •    Replicated Join
    •    Composite Join
    •    Cartesian Product
Working with HIVE
  •   HIVE Architecture and Components
    •    HIVE Datatypes
    •    HIVE Data models and Tables
    •    Buckets in HIVE
  •   Serialization and Deserialization
  •   HIVE File Formats
  •   HIVE Query Language (HQL)
  •   RC File Implementation
  •   SerDe
  •   Functions in HIVE
    •    Built-in Functions
    •    MapReduce Statements
    •    User Defined Functions
    •    Other Functions
  •   Case Studies
Working with PIG
  •   Components of PIG
  •   Data Models & Nested Data Models
  •   PIG Latin Overview
  •   Data types in PIG
  •   Running PIG Latin Scripts
    •   Interactive Mode
    •   Batch Mode
  •   PIG Execution Modes
    •   Local mode
    •   MapReduce Mode
  •   PIG vs SQL
  •   Operators and Functions
    •   Relational Operators
    •   Diagnostic Operator
    •   Eval Functions
    •   Complex Data Types
    •   Parameter Substitution
    •   User Defined Functions
    •   Additional PIG Libraries
  •   Installing and Testing PIG Engine
    •   Environmental Set-up for PIG Latin
    •   Load and Store Method
    •   Various PIG Commands
  •   Case Studies
Working with HBase
  • HBase Architecture and Components
  • Storage Model of HBase
  • HBase Vs RDBMS
  • Installing Configuring and Testing HBase
  • HBase Shell Commands
  • Case Studies
Zookeeper, Sqoop and Flume
  • Introduction to Zookeeper
    •   Challenges faced in Distributed application
    •   Goals and Use of Zookeeper
    •   Zookeeper Entities
    •   Zookeeper Data Model
    •   Client API Functions
    •   Case Studies
  • Introduction to Sqoop
    •   Sqoop Process
    •   Execute Process
    •   Import Process
    •   Export Process
    •   Sqoop Connectors & Commands
    •   Installing & Testing Sqoop
    •   Case Studies
    • Introduction to Flume
    •   Flume Models
    •   Flume Goals
    •   Scalabilities in Flume
    •   Case Studies
Apache Hadoop Ecosystem
  •   Ecosystem and Components
  •   Filesystem Components
  •   Data Storage Components
  •   Serialization Components
  •   Job Execution Components
  •   Work Management Components
  •   Operations and Development Components
  •   Security Components
  •   Data Transfer Components
  •   Data Interaction Components
  •   Analytics and Intelligence Components
  •   Search Framework Components
  •   Graph Processing Components
Introducing Oozie, Mahout and Spark
  •   Introduction to Apache Oozie
    • Apache Oozie Workflow
  •   Introduction to Mahout
    •    Features of Mahout
    •    Usage of Mahout
  •   Introduction to Apache Spark
    •    Tools of Apache Spark
    •    Key Concepts of Apache Spark
    •    Building an Application in Apache Spark
Hadoop Administration Troubleshooting and Security
    •   Typical Hadoop Core Cluster
    •   Load Balancer
    •   Programming Commands
    •   Configuration Files of Hadoop
    •   Hadoop Default - XML
    •   Critical Parameter
      •    Cluster Critical Parameters
      •    DFS Operations Parameters
      •    Port Numbering
    •   Hadoop Performance
      •    Monitoring
      •    Tuning
      •    Parameters
    •   Troubleshooting and Log Observations
    •   Introduction to Apache Ambari
      •    Hadoop Security Kerberos
      •    Authentication Mechanism
      •    Configuration Steps
      •    Data Confidentiality
(51 Ratings)
  • 5 Star
  • 4 Star
  • 3 Star
  • 2 Star
  • 1 Star
  • I'm very happy to be the student of this institution, and the guide we got is very experienced and they trying to teach us the things from ground level.

  • More complex things are made to solve in an easier and understandable ways by Kiran Sir...Iam extremely happy to be the part of the internship...Thank you!

  • Internship was really good and we learnt many new things of current technology.