Information which are very large in size is called Big Data. Typically we chip away at information of size MB(WordDoc ,Excel) or most extreme GB(Movies, Codes) however information in Peta bytes for example 10^15 byte size is called Big Data. It is expressed that practically 90% of the present information has been produced in the previous 3 years.
Wellsprings of Big Data
These information originate from numerous sources like
Social networking sites: Facebook, Google, LinkedIn every one of these destinations creates tremendous measure of information on an everyday premise as they have billions of clients around the world.
E-commerce site: Sites like Amazon, Flipkart, Alibaba creates immense measure of logs from which clients purchasing patterns can be followed.
Climate Station: All the climate station and satellite gives very enormous information which are put away and controlled to estimate climate.
Telecom organization: Telecom mammoths like Airtel, Vodafone study the client patterns and as needs be distribute their arrangements and for this they store the information of its million clients.
Share Market: Stock trade over the world creates immense measure of information through its day by day exchange.
3V’s of Big Data
Speed: The information is expanding at a very quick rate. It is assessed that the volume of information will twofold in every 2 years.
Assortment: Now a days information are not put away in lines and section. Information is organized just as unstructured. Log record, CCTV film is unstructured information. Information which can be spared in tables are organized information like the exchange information of the bank.
Volume: The measure of information which we manage is of very large size of Peta bytes.
Hadoop is an open source system from Apache and is utilized to store process and investigate information which are colossal in volume.Hadoop is written in Java and isn’t OLAP (online logical preparing). It is utilized for group/disconnected processing.It is being utilized by Facebook, Yahoo, Google, Twitter, LinkedIn and some more. Additionally it very well may be scaled up just by including hubs in the group.
Modules of Hadoop
HDFS: Hadoop Distributed File System. Google distributed its paper GFS and based on that HDFS was created. It expresses that the documents will be broken into squares and put away in hubs over the conveyed design.
Yarn: Yet another Resource Negotiator is utilized for work booking and deal with the bunch.
Guide Reduce: This is a structure which encourages Java projects to do the equal calculation on information utilizing key worth pair. The Map task takes input information and changes over it into an informational collection which can be processed in Key worth pair. The yield of Map task is devoured by decrease errand and afterward the out of reducer gives the ideal outcome.
Hadoop Common: These Java libraries are utilized to begin Hadoop and are utilized by other Hadoop modules.
The Hadoop design is a bundle of the document framework, MapReduce motor and the HDFS (Hadoop Distributed File System). The MapReduce motor can be MapReduce/MR1 or YARN/MR2.
A Hadoop group comprises of a solitary ace and various slave hubs. The ace hub incorporates Job Tracker, Task Tracker, NameNode, and DataNode while the slave hub incorporates DataNode and TaskTracker.
Financial Statements 