Basics of Hadoop
It is not a news anymore that the field of Big Data is gradually turning into the newest, rich kid on the block. With so many business experts and IT gurus not only welcoming but also praising this new amazing field of technology and development. The possibility that you can now store the entire data of a town and actually derive insights from it, is not only novel but so surprising that it is almost unbelievable sometimes. There are a lot of statistics and studies which state that the global revenues for the big data industry are going to be on a steady rise and would reach $150.8 billion by the end of 2017.
When this field of big data first emerged and when people heard of this concept for the first time, it was difficult to really comprehend as to how the companies would be able to store so much data and not just store but utilize it as well. This data that we talk about, is actually the information that comes from various social media applications that people use. From various accounts such as Facebook, Instagram, Twitter as well as search engines such as Google and even other aspects that formed a part of the Internet of Things. As people began to use these applications, the volume of data being collected began to go on increasing.
As this humongous amount of data kept on increasing, there were very few ways to store and ever fewer ways to actually process the same. There were a few systems that were able to work with these databases. These were popularly known as the Traditional Database Management Systems or abbreviated as DBMSs. This was comparatively an old system which was still unable to work with the kind of progress that was being made in the otherwise expanding field of Big Data. Data could be divided into two types, unstructured and structures, the former kind of data was very difficult to be processed by rudimentary technologies.
This is why an advanced technology of DBMS emerged to fill the gaps, which today has come to be popularly known as the Hadoop data analytics programming. The inception and initial stages of Hadoop were in the form of the Google file system and the idea for the same was first discussed in the year 2003. Three years later the development process accelerated and transformed into an open source project which was later spearheaded by the Apache Software Foundation. Today, Hadoop is rapidly becoming popular as an open source database management system for processing enormous data sets using the programming tool called MapReduce. There are also many prominent clusters on which this software runs, like Cloudera Inc, Hortonworks Inc, MapR Technologies and so on.
Hadoop has attracted quite a number of data aspirants who wish to make a career in the field of Data Science and Data Analytics. There are many professional training institutes like Imarticus Learning today offering Hadoop training to many who would like to jumpstart their career as well.
When this field of big data first emerged and when people heard of this concept for the first time, it was difficult to really comprehend as to how the companies would be able to store so much data and not just store but utilize it as well. This data that we talk about, is actually the information that comes from various social media applications that people use. From various accounts such as Facebook, Instagram, Twitter as well as search engines such as Google and even other aspects that formed a part of the Internet of Things. As people began to use these applications, the volume of data being collected began to go on increasing.
As this humongous amount of data kept on increasing, there were very few ways to store and ever fewer ways to actually process the same. There were a few systems that were able to work with these databases. These were popularly known as the Traditional Database Management Systems or abbreviated as DBMSs. This was comparatively an old system which was still unable to work with the kind of progress that was being made in the otherwise expanding field of Big Data. Data could be divided into two types, unstructured and structures, the former kind of data was very difficult to be processed by rudimentary technologies.
This is why an advanced technology of DBMS emerged to fill the gaps, which today has come to be popularly known as the Hadoop data analytics programming. The inception and initial stages of Hadoop were in the form of the Google file system and the idea for the same was first discussed in the year 2003. Three years later the development process accelerated and transformed into an open source project which was later spearheaded by the Apache Software Foundation. Today, Hadoop is rapidly becoming popular as an open source database management system for processing enormous data sets using the programming tool called MapReduce. There are also many prominent clusters on which this software runs, like Cloudera Inc, Hortonworks Inc, MapR Technologies and so on.
Hadoop has attracted quite a number of data aspirants who wish to make a career in the field of Data Science and Data Analytics. There are many professional training institutes like Imarticus Learning today offering Hadoop training to many who would like to jumpstart their career as well.
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