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What Is Big Data and Why Use Hadoop?

What is Big Data and Why Use Hadoop? Big data specifies datasets that are very big. It’s a hoard of large datasets that cannot be processed by the traditional methods of computing. Big data is related to a complete subject rather than merely data that can be processed using various techniques, tools, and framework. Hadoop is an open-source frame, which is based on Java Programming and supports the storage and processing capabilities of extremely large datasets in a computing environment that is distributed across branches. Hadoop was developed by a team of computer scientists, which comprised of Mike Cafarella and Doug Cutting in 2005, to support the distribution capabilities of search engines. There are pros & cons in hadoop, but compared to pros, cons are negotiable. Benefits of Hadoop • Scalable: Hadoop is a storage platform that is highly scalable, as it can easily store and distribute very large datasets at a time on servers that could be operated in parallel. • Cost effective: Hadoop is very cost-effective compared to traditional database-management systems. • Fast: Hadoop manages data through clusters, thus providing a unique storage method based on distributed file systems. Hadoop’s unique feature of mapping data on the clusters provides a faster data processing. • Flexible: Hadoop enables enterprises to access and process data in a very easy way to generate the values required by the company, thereby providing the enterprises with the tools to get valuable insights from various types of data sources operating in parallel. • Failure resistant: One of the great advantages of Hadoop is its fault tolerance. This fault resistance is provided by replicating the data to another node in the cluster, thus in the event of a failure, the data from the replicated node can be used, thereby maintaining data consistency. Careers with Hadoop Big data with Hadoop training could make a great difference in getting your dream career. Employees with capabilities of handling big data are considered more valuable to the organisation. Hadoop skills are in great demand and thus it is very important for the IT professionals to keep up with the current trend, because the amount of data generated day by day is ever increasing. According to the Forbes magazine report of 2015, around 80% of the global organisations are reported to make high- or medium-level investments in big data analytics. They consider this investment to be very significant and so they plan to increase their investment in big data analytics. There are more job opportunities with Hadoop. Looking at the market forecast for Big Data, it looks like the need for Big Data engineers is going to increase. Big Data is here to stay, as the data is ever increasing and does not seem to slow down in the coming years. A research conducted by the Avendus Capital reported that the IT market in India for big data is hovering near $1.15 billion in the year 2015. Big data analytics contributed for about one-fifth of the nation’s KPO market, which is considered to be worth almost $5.6 billion. The Hindu also predicted that by the end of year 2018, India alone would be facing a shortage of almost quarter million Big Data scientists. Therefore, Big Data Analysis with Hadoop presents a great career and tremendous growth opportunity.

What Is Big Data and Why Use Hadoop?

16K
What Is Big Data and Why Use Hadoop?

What is Big Data and Why Use Hadoop?

Big data specifies datasets that are very big. It’s a hoard of large datasets that cannot be processed by the traditional methods of computing. Big data is related to a complete subject rather than merely data that can be processed using various techniques, tools, and framework. Hadoop is an open-source frame, which is based on Java Programming and supports the storage and processing capabilities of extremely large datasets in a computing environment that is distributed across branches. Hadoop was developed by a team of computer scientists, which comprised of Mike Cafarella and Doug Cutting in 2005, to support the distribution capabilities of search engines. There are pros & cons in hadoop, but compared to pros, cons are negotiable.

Benefits of Hadoop

• Scalable: Hadoop is a storage platform that is highly scalable, as it can easily store and distribute very large datasets at a time on servers that could be operated in parallel.

• Cost effective: Hadoop is very cost-effective compared to traditional database-management systems.

• Fast: Hadoop manages data through clusters, thus providing a unique storage method based on distributed file systems. Hadoop’s unique feature of mapping data on the clusters provides a faster data processing.

• Flexible: Hadoop enables enterprises to access and process data in a very easy way to generate the values required by the company, thereby providing the enterprises with the tools to get valuable insights from various types of data sources operating in parallel.

• Failure resistant: One of the great advantages of Hadoop is its fault tolerance. This fault resistance is provided by replicating the data to another node in the cluster, thus in the event of a failure, the data from the replicated node can be used, thereby maintaining data consistency.

Careers with Hadoop

Big data with Hadoop training could make a great difference in getting your dream career. Employees with capabilities of handling big data are considered more valuable to the organisation. Hadoop skills are in great demand and thus it is very important for the IT professionals to keep up with the current trend, because the amount of data generated day by day is ever increasing.

According to the Forbes magazine report of 2015, around 80% of the global organisations are reported to make high- or medium-level investments in big data analytics. They consider this investment to be very significant and so they plan to increase their investment in big data analytics.

There are more job opportunities with Hadoop. Looking at the market forecast for Big Data, it looks like the need for Big Data engineers is going to increase. Big Data is here to stay, as the data is ever increasing and does not seem to slow down in the coming years.

A research conducted by the Avendus Capital reported that the IT market in India for big data is hovering near $1.15 billion in the year 2015. Big data analytics contributed for about one-fifth of the nation’s KPO market, which is considered to be worth almost $5.6 billion. The Hindu also predicted that by the end of year 2018, India alone would be facing a shortage of almost quarter million Big Data scientists. Therefore, Big Data Analysis with Hadoop presents a great career and tremendous growth opportunity.

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6 comments

Ruben Mundschenk 02 Feb 2017

I truly appreciate this post. I¦ve been looking everywhere for this! Thank goodness I found it on Bing. You've made my day! Thank you again

OoaUtern 31 Mar 2017

thank you

Tony 05 May 2017

Hi, Thanks for sharing the information. These information will really help us a lot..

vijaya 22 Mar 2018

Hi, Thanks for Providing the information. These information will really help us a lot

Anuroop 22 Mar 2018

very good blog.Very useful to hadoop related people.Actually iam new to hadoop.this is so useful to me

muruganram 05 Apr 2018

Thanks Share My Website.

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Types Of Big Data

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Diagram showing Semi-structured data Difference between Structured, Semi-structured and Unstructured data       Factors      Structured data       Semi-structured data     Unstructured data Flexibility It is dependent and less flexible It is more flexible than structured data but less than flexible than unstructured data It is flexible in nature and there is an absence of a schema Transaction Management Matured transaction and various concurrency technique The transaction is adapted from DBMS not matured No transaction management and no concurrency Query performance Structured query allow complex joining Queries over anonymous nodes are possible An only textual query is possible Technology It is based on the relational database table It is based on RDF and XML This is based on character and library data Big data is indeed a revolution in the field of IT. The use of Data analytics is increasing every year. In spite of the demand, organizations are currently short of experts. 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How to Install Spark on Ubuntu

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.In this article, we will cover the installation procedure of Apache Spark on the Ubuntu operating system.PrerequisitesThis guide assumes that you are using Ubuntu and Hadoop 2.7 is installed in your system.Java8 should be installed in your Machine.Hadoop should be installed in your Machine.System requirementsUbuntu OS Installed.Minimum of 8 GB RAM.At least 20 GB free space.Installation ProcedureMaking system readyBefore installing Spark ensure that you have installed Java8 in your Ubuntu Machine. If not installed, please follow below process to install java8 in your Ubuntu System.a. 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How to install Apache Spark on Windows?

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