Big Data: Data Storage Technology

Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy.

Big data creates big career opportunities:

"Big data is becoming an effective basis of competition in pretty much every industry"

How Big Data Analytics is the Best Career move?

  • Soaring Demand for Analytics Professionals - There are many job opportunities in Big Data management and Analytics now than last year and most of the IT professionals are prepared to invest time and money for the training.
  • Huge Job Opportunities & Meeting the Skill Gap - The US will face a shortage of about 190,000 data scientists around 2018 Salary Aspects
  • Big Data Analytics: A Top Priority in a lot of Organizations
  • Growth in Adoption of Big Data Analytics
  • Analytics: A Key Factor in Decision Making
  • Analytics is a key competitive resource for many companies.
  • Big Data Analytics is Used Everywhere!
  • Surpassing Market Forecast / Predictions for Big Data Analytics

Numerous Choices in Job Titles and Type of Analytics

  • Big Data Analytics Business Consultant
  • Big Data Analytics Architect
  • Big Data Engineer
  • Big Data Solution Architect
  • Big Data Analyst
  • Analytics Associate
  • Business Intelligence and Analytics Consultant
  • Metrics and Analytics Specialist

Why Bigdata?

IT executives continuously evaluate the technological trends that impact their business. Some merely deploy technology, to progress in the goals spelt out in the business plans. Big Data is used

  • To Manage Data Better
  • To Benefit in Speed, Capacity and Scalability of Cloud Storage
  • End Users Can Visualize Data
  • Companies Can Find New Business Opportunities
  • Data Analysis, Capabilities Will Evolve
  • Create new products and services for customers
  • Faster, better decision making
  • Cost reduction

Why Hadoop:

Hadoop is an open source, Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation

Bigdata and Hadoop Developer:

Introduction to Big Data and Hadoop
  • Data explosion and the need for Big Data
  • Concept of Big Data
  • Basics of Hadoop
  • History and milestones of Hadoop
  • How to use Oracle Virtual Box to open a VM
Hadoop Architecture
  • Use of Hadoop in commodity hardware
  • Various configurations and services of Hadoop
  • Difference between a regular and a Hadoop Distributed File System
  • HDFS architecture
Hadoop Deployment
  • Steps to install Hadoop
  • Steps involved in single and multi-node Hadoop
  • Steps to perform clustering of the Hadoop environment Introduction to YARN and MapReduce
  • YARN architecture
  • Different components of YARN
  • Concepts of MapReduce
  • Steps to install Hadoop in Ubuntu machine
  • Roles of user and system
Advanced HDFS and MapReduce
  • Advanced HDFS and related concepts
  • Steps to decommission a DataNode
  • Advanced MapReduce concepts
  • Various joins in MapReduce
Pig
  • Concepts of Pig
  • Installation of a Pig engine
  • Prerequisites for the preparation of the environment for Pig Latin
Hive
  • Hive and its importance
  • Hive architecture and its components
  • Steps to install and configure Hive
  • Basics of Hive programming
HBase
  • HBase architecture
  • HBase data model
  • Steps to install HBase
  • How to insert data and query data from HBase
Commercial Distribution of Hadoop
  • Major commercial distributions of Hadoop
  • Cloudera Quickstart Virtual Machine or VM
  • Hue interface
  • Cloudera Manager interface
Sqoop, and Flume
  • ZooKeeper and its role
  • Challenges faced in distributed processing
  • Install and configure ZooKeeper
  • Concept of Sqoop
  • Configure Sqoop
  • Concept of Flume
  • Configure and run Flume
Zoo Keeper and OOZIE Overview
Classroom Training: 15 Days (30 Hours)

EMC Proven Professional is the #1 certification program in the information storage and management industry. Being Proven means investing in yourself and formally validating your knowledge, skills, and expertise by the industry's most comprehensive learning and certification program. The Data Science and Big Data Analytics course prepares you for Data Scientist Associate (EMCDSA) Certification.

  • Test number: E20-007
  • Testing time: 90 min.
  • Numbers of question: 60 questions

Java knowledge with Eclipse IDE and Linux basic commands (Especially file handling concepts)

The Reason for Java

  • Apache Hadoop framework is written in Java
  • Have to execute the codes in the Java with Eclipse IDE
  • The Hadoop Common package contains the necessary Java ARchive (JAR) files and scripts needed to start Hadoop.
  • Hadoop requires Java Runtime Environment (JRE) 1.7 or above.
  • The Hadoop distributed file system (HDFS) is a distributed, scalable, and portable file-system written in Java for the Hadoop framework
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