Hadoop Certification Course Overview

Data is information in raw format. With increasing data size, it has become the need for inspecting, cleaning, transforming, and modeling data with the goal of finding useful information, making conclusions, and supporting decision making. This process is known as Big Data analysis.

Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements.

Data analytics is also known as data analysis.

Data analytics is primarily conducted in business-to-consumer (B2C) applications. Global organizations collect and analyze data associated with customers, business processes, market economics or practical experience. Data is categorized, stored and analyzed to study purchasing trends and patterns. 

Evolving data facilitates thorough decision-making. For example, a social networking website collects data related to user preferences, community interests, and segment according to specified criteria such as demographics, age or gender. The proper analysis reveals key user and customer trends and facilitates the social network’s alignment of content, layout and overall strategy.

What is Data Analysis?

Data mining is a particular data analysis technique where modeling and knowledge discovery for predictive rather than purely descriptive purposes is focused. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide business analytics into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA).

EDA focuses on discovering new features in the data and CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics does forecasting or classification by focusing on statistical or structural models while in text analytics, statistical, linguistic and structural techniques are applied to extract and classify information from textual sources, a species of unstructured data. All are varieties of the data analysis.

The Big Data wave has changed ways in which industries function. With Big Data has emerged the requirement to implement advanced analytics to it. Now experts can make more accurate and profitable decisions.

Course Objectives:

• How to write MapReduce programs using the Hadoop API.
• What is Hadoop and how can it help process large data sets.
• How to ingest data from an RDBMS or a data warehouse to Hadoop.
• Best practices for building, debugging and optimizing Hadoop solutions.
• How to use HDFS (the Hadoop Distributed Filesystem), from the command line and API.
• Get introduced to tools like Pig, Hive, HBase, Elastic MapReduce etc.

This Course is Designed to Benefit the Following Professionals:

• A developer who wants to learn Hadoop 
• A team that has decided to migrate from a RDBMS or a traditional data warehouse to Hadoop.
• A team that is struggling to extract insights from large scale and fast growing data in traditional systems.

Big data Hadoop  Syllabus

• Introduction
  -Big Data
  -Data Science
  -Hadoop Hands-on: Install and configure a multi node Hadoop cluster with Ambari
• Data Storage
  -File System Abstraction
  -Big Data and Distributed File Systems
  -Hadoop Distributed File System (HDFS) Hands-on: Manipulating files in HDFS using hadoop fs commands.
  -Hands-on: Manipulating files in HDFS pragmatically using the FileSystem API.
  -Alternative Hadoop File Systems: IBM GPFS, MapR-FS, Lustre, Amazon S3 etc.
• Data Processing
  -MapReduce Hands-on: Write a simple log analysis MapReduce application Hands-on: Write an Inverted Index
  -MapReduce Application with custom Partitioner and Combiner Hands-on: Writing a streaming MapReduce job in Python.
• YARN and Hadoop 2.0

-Data Integration

-Integrating Hadoop into your existing enterprise. 

-Introduction to Sqoop Hands-on: Importing data from an RDBMS to HDFS using Sqoop Hands-on:Exporting data from HDFS to an RDBMS

-Other data integration tools: Flume, Kafka, Informatica, Talend etc.

• Higher Level Tools

-Defining workflows with Oozie

-An introduction to Hive

-An introduction to Pig

-An introduction to HBase