Useful Big Data And Hadoop For Managing Large Volume Of Data

About Big Data Analytics

The process of examining different and large data sets popularly called as big data is called as big data analytics. This is processed to discover the hidden patterns, current market trends, customer performances, unknown correlations and some other information which help an organization to make best business decisions. It is done through specialized software programs and systems. It provides the way to different benefits to the business which includes new revenue opportunities, improved customer service, effective marketing, competitive advantages and improved operational efficiency. A number of big data analytics workshops provide knowledge on big data.

The applications of big data analytics allow data scientists, predictive modelers, statisticians and other professionals of analytics field to analyze the developing volumes of structured transaction data. In addition, it allows other forms of data that are often left unused by conventional business intelligence and analytics programs.

About Hadoop

This is one of the powerful platforms which is generally used to process a huge amount of data. It uses popular big data analytics tools such as Yarn, Hive, MapReduce, Spark, Kafka, Pig, HBase to process such large amount of data. The big data and Hadoop online training provide knowledge on both big data management and Hadoop platform.

It is a java-based, open source programming framework which supports the processing and storage of a high volume of data sets in a distributed computing environment. It is the part of Apache project and this is provided by the Apache software foundation.

Learning big data analytics and Hadoop include concepts such as

  • Basics of Big Data
  • Data Analytics
  • Big Data Challenges
  • Technologies supported by big data
  • Future of Hadoop
  • The Hadoop Distributed File System
  • Anatomy of a Hadoop Cluster
  • Breakthroughs of Hadoop
  • Name Node
  • Data Node
  • Secondary Name Node
  • Job Tracker
  • Task Tracker
  • Blocks and Input Splits
  • Data Replication
  • Hadoop Rack Awareness
  • Cluster Architecture and Block Placement
  • Accessing HDFS
  • JAVA Approach
  • CLI Approach

What will the participants learn?

In such training, participants will learn about

  • The fundamentals of Apache Hadoop and data ETL that is extract, transform, load, ingestion, and processing with Hadoop tools
  • How to join multiple data sets and analyze disparate data with Pig
  • How to organize data into tables, perform transformations, and simplify complex queries with Hive
  • How to do real-time communicative analyses on big datasets that are kept in HDFS or HBase using SQL with Impala
  • How to pick the best tool for a given task in Hadoop, achieve interoperability, and manage workflows that are repetitive

Who can learn big data analytics?

Big data analytics and Hadoop can be learned by

  • Data analysts
  • Business analysts
  • Administrators
  • Developers

These two concepts are also useful for professionals those who have prior experience with SQL and UNIX commands and basic SQL. Learning this will help professionals to efficiently work with big data and perform operations and manipulations on data of an organization.