MindsMapped Core Java Training

BIG DATA and HADOOP Training

MindsMapped Online IT Training Reviews

Big Data/Hadoop Training in Iowa by MindsMapped

  • Attend Big Data and Hadoop training in Iowa. Our instructor led classroom training in Iowa is designed/created to assist you to get complete understanding of tasks that need to be done as a Hadoop Developer.
  • Learn about key aspects of Hadoop programming in our Hadoop training program. This training program is in line with the Apache Hadoop guidelines.
  • On completion of Big Data and Hadoop training course in Iowa, you are ready to appear and succeed in Hadoop job interviews and/or pass the Hadoop certifications from Cloudera, HortonWorks, and MapR.
  • Online Big Data and Hadoop training in Iowa is conducted by certified professionals who have wide range of on-the-job experience and/or subject matter experts.
  • This instructor-led online Hadoop tutorial classes in Iowa, IA covers MapReduce, HDFS, Pig, Hive, MR Scripts, HBase, NoSQL, Zookeeper, Oozie, Sqoop, Flume, Yarn, Scala, Spark and other related topics.

Key Features:

30 hours of Instructor led training

Industry based Project work

Life time access to Knowledge Base

Hands on Project Execution with Cloudera

Resume Preparation and Mock Interviews

Get Hadoop Certified

Who are the Hadoop Class Trainers at MindsMapped?

  • Our Hadoop Instructors are full time employees working as Architects, Technical Leads or Managers for Fortune 500 companies
  • Our Trainers are passionate about teaching and conduct these sessions for MindsMapped
  • Their experience and knowledge helps them bring real world projects and scenarios to the Hadoop classes
  • Instructors ensure that online classes are lively and participative making learning a pleasure

Big Data Hadoop Course Curriculum

Click here to download Big Data Hadoop Developer Course Contents
  • Session 1:

    • 1. Importance of Data
    • 2. What is BigData and its Hype
    • 3. Definition of Big Data - Structured Vs Unstructured data
    • 4. Users of Big Data - Scenarios and Challanges
    • 5. Why Distributed Processsing?
    • 6. Introduction to Hadoop - History & its Ecosystem
    • 7. Hadoop Animal Planet - When to use and when not to use Hadoop

    Session 2:

    • 1. What is Hadoop? - Key distinctions of Hadoop
    • 2. Hadoop Components and Architectures
    • 3. Understanding storage & processing components
    • 4. Anatomy of a File Write & File Read
    • 5. Handout discussion & Walkthrough of CDH Setup
    • 6. Hadoop Cluster modes & Configuration Files
    • 7. Understanding Hadoop Cluster & Data Ingestion to HDFS

    Session 3:

    • 1. Meet MapReduce
    • 2. WordCount Algorithm, Distributed System & Drawbacks
    • 3. MapReduce approach - Input & Output forms of a MR Program
    • 4. Phases of MR Algorithm - Map & Reduce
    • 5. Workflow & TRansformation of Data
    • 6. Walkthrough on Word Count Code
    • 7. Input Split & HDFS Block and its Relation

    Session 4:

    • 1. MR Flow with Single Reduce Task & with Multiple Reducers
    • 2. Data Locality Optimization
    • 3. Speculation Execution
    • 4. Combiner & Partioner(Hash Algorithm)
    • 5. Hadoop & Custom Data Types
    • 6. Input Format & Hierarchy
    • 7. Output Format & Hierarchy
  • Session 5:

    • 1. Side Data distribution - Distributed cache
    • 2. Joins - Map side join & Reduce Side join
    • 3. MR Unit - An Unit testing framework
    • 4. Introduction to Pig
    • 5. Pig Vs SQL
    • 6. Execution modes & Running Pig
    • 7. Pig Data types

    Session 6:

    • 1. Pig Relational & Diagnostic Operators
    • 2. Multi Query Execution
    • 3. Macro & UDF statements
    • 4. Commands & Expression
    • 5. Pig - Schemas & Functions Used
    • 6. Pig Latin File Loaders
    • 7. Pig UDF & executing a Pig UDF

    Session 7:

    • 1. Introduction to Hive - Pig Vs Hive
    • 2. Limitations, Possibilities & its Architecture
    • 3. Metastore & Data Organisation
    • 4. Hive QL - SQL Vs Hive QL
    • 5. Hive Data types
    • 6. Managed & External tables
    • 7. Partitions & Buckets

    Session 8:

    • 1. Storage Formats
    • 2. Built-in Serdes
    • 3. Importing Data & Usage of Alter,Drop commands
    • 4. Data Querying
    • 5. Using MR Scripts
    • 6. Hive Joins, Views & Sub Queries
    • 7. Hive UDFs
  • Session 9:

    • 1. NoSql & HBase
    • 2. Row & Column Oriented storage
    • 3. What is HBase?
    • 4. HBase & Shell commands
    • 5. HBase operations - Java
    • 6. HBase operations - MR
    • 7. NoSql-MongoDB

    Session 10:

    • 1. Introduction to Zookeeper
    • 2. Distributed Coordination
    • 3. Zookeper Data Model & Service
    • 4. Zookeper in HBase
    • 5. Introduction to Oozie
    • 6. Oozie workflow

    Session 11:

    • 1. Partitions & Buckets
    • 2. Storage Formats
    • 3. Built-in Serdes
    • 4. Importing Data
    • 5. Alter & Drop Commands
    • 6. Data Querying

    Session 12:

    • 1. Introduction to Sqoop
    • 2. Sqoop design & Commands
    • 3. Sqoop Import & Export
    • 4. Sqoop Incremental load
    • 5. Introduction to Flume
    • 6. Architecture & its Components
    • 7. Flume Configuration & Interceptors
  • Session 13:

    • 1. Hadoop 1 Limitations
    • 2. HDFS Federation
    • 3. NameNode High Availability
    • 4. Introduction to YARN
    • 5. YARN Applications
    • 6. YARN Architecture
    • 7. Anatomy of an YARN application

    Session 14:

    • 1. Installing Hadoop 2.2 on the Ubuntu
    • 2. Eclipse and Maven
    • 3. Configuration files
    • 4. Installation of Pig,Hive,Sqoop,Flume,oozie and zookeper
    • 5. Installation of NoSql database - HBase
    • 6. Hadoop Commands

    Session 15:

    • 1. What is Big Data?
    • 2. What is Spark?
    • 3. Why Spark?
    • 4. Spark Ecosystem
    • 5. A note about Scala
    • 3. Why Scala?
    • 4. MapReduce vs Spark
    • 5. Hello Spark!
  • Session 16:

    • 1. Java to MapReduce Conversion
    • 2. MapReduce Project

    Session 17:

    • 1. Hive Project
    • 2. Pig Project
  • Session 1:

    • 1. Importance of Data
    • 2. What is BigData and its Hype
    • 3. Definition of Big Data - Structured Vs Unstructured data
    • 4. Users of Big Data - Scenarios and Challanges
    • 5. Why Distributed Processsing?
    • 6. Introduction to Hadoop - History & its Ecosystem
    • 7. Hadoop Animal Planet - When to use and when not to use Hadoop
  • Session 2:

    • 1. What is Hadoop? - Key distinctions of Hadoop
    • 2. Hadoop Components and Architectures
    • 3. Understanding storage & processing components
    • 4. Anatomy of a File Write & File Read
    • 5. Handout discussion & Walkthrough of CDH Setup
    • 6. Hadoop Cluster modes & Configuration Files
    • 7. Understanding Hadoop Cluster & Data Ingestion to HDFS
  • Session 3:

    • 1. Meet MapReduce
    • 2. WordCount Algorithm, Distributed System & Drawbacks
    • 3. MapReduce approach - Input & Output forms of a MR Program
    • 4. Phases of MR Algorithm - Map & Reduce
    • 5. Workflow & TRansformation of Data
    • 6. Walkthrough on Word Count Code
    • 7. Input Split & HDFS Block and its Relation
  • Session 4:

    • 1. MR Flow with Single Reduce Task & with Multiple Reducers
    • 2. Data Locality Optimization
    • 3. Speculation Execution
    • 4. Combiner & Partioner(Hash Algorithm)
    • 5. Hadoop & Custom Data Types
    • 6. Input Format & Hierarchy
    • 7. Output Format & Hierarchy
  • Session 5:

    • 1. Side Data distribution - Distributed cache
    • 2. Joins - Map side join & Reduce Side join
    • 3. MR Unit - An Unit testing framework
    • 4. Introduction to Pig
    • 5. Pig Vs SQL
    • 6. Execution modes & Running Pig
    • 7. Pig Data types
  • Session 6:

    • 1. Pig Relational & Diagnostic Operators
    • 2. Multi Query Execution
    • 3. Macro & UDF statements
    • 4. Commands & Expression
    • 5. Pig - Schemas & Functions Used
    • 6. Pig Latin File Loaders
    • 7. Pig UDF & executing a Pig UDF
  • Session 7:

    • 1. Introduction to Hive - Pig Vs Hive
    • 2. Limitations, Possibilities & its Architecture
    • 3. Metastore & Data Organisation
    • 4. Hive QL - SQL Vs Hive QL
    • 5. Hive Data types
    • 6. Managed & External tables
    • 7. Partitions & Buckets
  • Session 8:

    • 1. Storage Formats
    • 2. Built-in Serdes
    • 3. Importing Data & Usage of Alter,Drop commands
    • 4. Data Querying
    • 5. Using MR Scripts
    • 6. Hive Joins, Views & Sub Queries
    • 7. Hive UDFs
  • Session 9:

    • 1. NoSql & HBase
    • 2. Row & Column Oriented storage
    • 3. What is HBase?
    • 4. HBase & Shell commands
    • 5. HBase operations - Java
    • 6. HBase operations - MR
    • 7. NoSql-MongoDB
  • Session 10:

    • 1. Introduction to Zookeeper
    • 2. Distributed Coordination
    • 3. Zookeper Data Model & Service
    • 4. Zookeper in HBase
    • 5. Introduction to Oozie
    • 6. Oozie workflow
  • Session 11:

    • 1. Partitions & Buckets
    • 2. Storage Formats
    • 3. Built-in Serdes
    • 4. Importing Data
    • 5. Alter & Drop Commands
    • 6. Data Querying
  • Session 12:

    • 1. Introduction to Sqoop
    • 2. Sqoop design & Commands
    • 3. Sqoop Import & Export
    • 4. Sqoop Incremental load
    • 5. Introduction to Flume
    • 6. Architecture & its Components
    • 7. Flume Configuration & Interceptors
  • Session 13:

    • 1. Hadoop 1 Limitations
    • 2. HDFS Federation
    • 3. NameNode High Availability
    • 4. Introduction to YARN
    • 5. YARN Applications
    • 6. YARN Architecture
    • 7. Anatomy of an YARN application
  • Session 14:

    • 1. Installing Hadoop 2.2 on the Ubuntu
    • 2. Eclipse and Maven
    • 3. Configuration files
    • 4. Installation of Pig,Hive,Sqoop,Flume,oozie and zookeper
    • 5. Installation of NoSql database - HBase
    • 6. Hadoop Commands
  • Session 15:

    • 1. What is Big Data?
    • 2. What is Spark?
    • 3. Why Spark?
    • 4. Spark Ecosystem
    • 5. A note about Scala
    • 3. Why Scala?
    • 4. MapReduce vs Spark
    • 5. Hello Spark!
  • Session 16:

    • 1. Java to MapReduce Conversion
    • 2. MapReduce Project
  • Session 17:

    • 1. Hive Project
    • 2. Pig Project
  • Session 1:

    • 1. Importance of Data
    • 2. What is BigData and its Hype
    • 3. Definition of Big Data - Structured Vs Unstructured data
    • 4. Users of Big Data - Scenarios and Challanges
    • 5. Why Distributed Processsing?
    • 6. Introduction to Hadoop - History & its Ecosystem
    • 7. Hadoop Animal Planet - When to use and when not to use Hadoop
  • Session 2:

    • 1. What is Hadoop? - Key distinctions of Hadoop
    • 2. Hadoop Components and Architectures
    • 3. Understanding storage & processing components
    • 4. Anatomy of a File Write & File Read
    • 5. Handout discussion & Walkthrough of CDH Setup
    • 6. Hadoop Cluster modes & Configuration Files
    • 7. Understanding Hadoop Cluster & Data Ingestion to HDFS
  • Session 3:

    • 1. Meet MapReduce
    • 2. WordCount Algorithm, Distributed System & Drawbacks
    • 3. MapReduce approach - Input & Output forms of a MR Program
    • 4. Phases of MR Algorithm - Map & Reduce
    • 5. Workflow & TRansformation of Data
    • 6. Walkthrough on Word Count Code
    • 7. Input Split & HDFS Block and its Relation
  • Session 4:

    • 1. MR Flow with Single Reduce Task & with Multiple Reducers
    • 2. Data Locality Optimization
    • 3. Speculation Execution
    • 4. Combiner & Partioner(Hash Algorithm)
    • 5. Hadoop & Custom Data Types
    • 6. Input Format & Hierarchy
    • 7. Output Format & Hierarchy
  • Session 5:

    • 1. Side Data distribution - Distributed cache
    • 2. Joins - Map side join & Reduce Side join
    • 3. MR Unit - An Unit testing framework
    • 4. Introduction to Pig
    • 5. Pig Vs SQL
    • 6. Execution modes & Running Pig
    • 7. Pig Data types
  • Session 6:

    • 1. Pig Relational & Diagnostic Operators
    • 2. Multi Query Execution
    • 3. Macro & UDF statements
    • 4. Commands & Expression
    • 5. Pig - Schemas & Functions Used
    • 6. Pig Latin File Loaders
    • 7. Pig UDF & executing a Pig UDF
  • Session 7:

    • 1. Introduction to Hive - Pig Vs Hive
    • 2. Limitations, Possibilities & its Architecture
    • 3. Metastore & Data Organisation
    • 4. Hive QL - SQL Vs Hive QL
    • 5. Hive Data types
    • 6. Managed & External tables
    • 7. Partitions & Buckets
  • Session 8:

    • 1. Storage Formats
    • 2. Built-in Serdes
    • 3. Importing Data & Usage of Alter,Drop commands
    • 4. Data Querying
    • 5. Using MR Scripts
    • 6. Hive Joins, Views & Sub Queries
    • 7. Hive UDFs
  • Session 9:

    • 1. NoSql & HBase
    • 2. Row & Column Oriented storage
    • 3. What is HBase?
    • 4. HBase & Shell commands
    • 5. HBase operations - Java
    • 6. HBase operations - MR
    • 7. NoSql-MongoDB
  • Session 10:

    • 1. Introduction to Zookeeper
    • 2. Distributed Coordination
    • 3. Zookeper Data Model & Service
    • 4. Zookeper in HBase
    • 5. Introduction to Oozie
    • 6. Oozie workflow
  • Session 11:

    • 1. Partitions & Buckets
    • 2. Storage Formats
    • 3. Built-in Serdes
    • 4. Importing Data
    • 5. Alter & Drop Commands
    • 6. Data Querying
  • Session 12:

    • 1. Introduction to Sqoop
    • 2. Sqoop design & Commands
    • 3. Sqoop Import & Export
    • 4. Sqoop Incremental load
    • 5. Introduction to Flume
    • 6. Architecture & its Components
    • 7. Flume Configuration & Interceptors
  • Session 13:

    • 1. Hadoop 1 Limitations
    • 2. HDFS Federation
    • 3. NameNode High Availability
    • 4. Introduction to YARN
    • 5. YARN Applications
    • 6. YARN Architecture
    • 7. Anatomy of an YARN application
  • Session 14:

    • 1. Installing Hadoop 2.2 on the Ubuntu
    • 2. Eclipse and Maven
    • 3. Configuration files
    • 4. Installation of Pig,Hive,Sqoop,Flume,oozie and zookeper
    • 5. Installation of NoSql database - HBase
    • 6. Hadoop Commands
  • Session 15:

    • 1. What is Big Data?
    • 2. What is Spark?
    • 3. Why Spark?
    • 4. Spark Ecosystem
    • 5. A note about Scala
    • 3. Why Scala?
    • 4. MapReduce vs Spark
    • 5. Hello Spark!
  • Session 16:

    • 1. Java to MapReduce Conversion
    • 2. MapReduce Project
  • Session 17:

    • 1. Hive Project
    • 2. Pig Project

Frequently Asked Big Data & Hadoop Questions

  • What is Big data?
  • Big Data is defined as a large volume of both structured and unstructured raw data that inundates an enterprise on a day-to-day basis. By using Big Data you can take data from any source and examine it to find answers like cost reductions, new product development, time reductions and smart decision making.

  • What is the average salary of a Hadoop Professional?
  • According to Dice, Hadoop professional made an average salary of $115,000 in 2015, which is slightly above the average of Big Data jobs.

  • What are the best certifications for Hadoop?
  • There are several top-grade big data vendors like Cloudera, Hortonworks, IBM, and MapReduce offering Hadoop Developer Certification and Hadoop Administrator Certification at different levels.

  • Do I have to be certified in Big Data and Hadoop?
  • Whether youre job hunting, waiting for a promotion, third-party proof of your skills is a great option. Certifications measure your skills and knowledge against industry to unlock great career opportunities as a Hadoop developer and to become an expert in Big Data Hadoop.

  • Is Java covered as part of this Big Data Hadoop course?
  • The total part of the Java is not covered in Big Data Hadoop course, the concepts which are required for understanding Big Data Hadoop course topics are covered.

  • What is MapReduce?
  • MapReduce is the heart of Hadoop. The MapReduce concept is simple to understand for those who are close with clustered out data processing solutions. It is the programming pattern that allows across hundreds or thousands of servers in a Hadoop cluster.

  • What is Cloud Lab?
  • Cloud Lab is a meta-cloud used in building cloud computing applications. This feature also allows users to store variables in the cloud. Cloud variables determine regular variables that have the characters in front of them.

  • What is HDFS?
  • The Hadoop Distributed File System (HDFS) is one of the most crucial topics of Apache Hadoop. It is the primary storage system used by Hadoop applications. HDFS is known as a Java-based file system that provides reliable data storage and high-performance access to data across Hadoop clusters.

  • What is Apache Flume?
  • Apache Flume is a reliable, distributed, and available service for aggregating, efficiently collecting and moving large amounts of streaming data into the Hadoop Distributed File System (HDFS).

  • What is Apache Hive?
  • Hive is a component of Hortonworks Data Platform (HDP). Apache Hive provides an SQL-like interface to store data in HDP. A command line tool and JDBC driver are used to connect users to Hive.

  • What is Sqoop
  • Sqoop is a tool designed to carry bulk data between Hadoop and database servers. It is also used to import data from databases such as Oracle to Hadoop HDFS, MySQL to Hadoop file system.

    Introduction to Big Data Hadoop - Demo Class Video

    What is Big Data Hadoop? Demo Class Video

    Online Big Data Hadoop Training by MindsMapped


Places where this course is available:

 


 

Need more info?

+1 (385) 743-0999 / (385) 237-9777
 

Hadoop Sample Resumes

Download Hadoop Sample Resume-1
Download Hadoop Sample Resume-2
Download Hadoop Sample Resume-3
Download Hadoop Sample Resume-4
Download Hadoop Sample Resume-5

Frequently Asked Questions

Training FAQs
Payment FAQs
Training Terms & Conditions

Refer a Friend and Get a $100 Discount


Big Data Hadoop Interview Questions




Read Testimonials / Reviews

"Interactive session, Notes and interview questions in knowledge base, Trainer was Friendly and made comfortable in class, Good teaching skill and clarifies doubts in detail. Co-operation of MindsMapped team in all aspects to serve the purpose."
Muralidhar

Other Training Courses

Business Analysis Training
QA / Software Testing Training
Java and J2EE Training
Core Java Training
Advance Java (J2EE) Training
Microsoft .NET Training
SCADA, DCS and PLC Training
View More Job Search Results
Call Us Now

Self Paced Learning

Learn Big Data and Hadoop at your own pace by getting access to all the Video Seminars by different Instructors.

Instructor Led Training

Instructor Led online training conducted by working professionals who bring real world knowledge, and examples to the class

Self Paced Learning

Learn Big Data and Hadoop at your own pace by getting access to all the Video Seminars by different Instructors.

Instructor Led Training

Instructor Led online training conducted by working professionals who bring real world knowledge, and examples to the class

 

CONTACT  US

 
ENROLL NOW
Call Us
(+1) 385 743 0999
(+1) 385 237 9777
Email Us
info@mindsmapped.com
Receive Call Back