What is Hadoop problem and solution in Big Data

What is Hadoop problem and solution in Big Data
Hadoop

Hadoop Big Data and this big data has become a major problem and so that's why it has become a subject for us to do the analysis to give the solutions and this Hadoop is the solution.

Big data Hadoop

Big Data is a problem and Hadoop is a solution so let us go for for the details we are going to discuss what is Apache Hadoop. Apogee Hadoop is nothing but one open-source framework and Hadoop can easily handle large amounts of data on its low-cost simple hardware cluster. cluster means it is a collection of multiple computers and low-cost computers mean here we are using the commodity nodes or commodity servers that means the harder cost is very low.

And that's why you are going to give a low-cost solution and this Hadoop is actually providing the solution for the big data and this Hadoop is scalable if you require to reduce the workload or divider or block with multiple servers then the workload can be divided in a horizontal scaling.

Hadoop can easily handle a large amount of data on a low-cost simple hardware cluster and Hadoop is also scalable and fault-tolerant framework fault-tolerant means if one computer goes down if one server goes down then another server will come into the play and from the user's point of view they will not fail any kind of failure theories.

The failure transparency the Hadoop is not only a storage system data can be processed using this respective framework data will be processed analysis can be done here the Hadoop system is basically written in Java language. Hadoop is an open-source tool from the Apache Software Foundation and as the open-source project, we can even change the source course of the Hadoop system.

As it is open source also we can change the code and most of the codes are written in Java language most of the Hadoop codes were written by Yahoo IBM cloud etc. these companies have written many codes in Hadoop open-source how-to provides parallel processing through different commodity Hardware simultaneously commodity harder means that cheap hardware with the help of which we can have the parallel processing.

  • The data processing and analysis will be done in every faster way as it works on commodity hardware. 
  • The cost is very low and the commodity hardware is low end and very cheap hardware.

The Hadoop solution is also economic and chip why we should use Hadoop so it is a very important and common question so the Hadoop solution is very popular and it has captured at least 90 percent of the big data market Hadoop has some unique features that make it the solution very much popular and Hadoop is scalable as we have discussed earlier to do the load sharing we can go on adding other community commodity servers. that's why Hadoop is scalable in that case so we can increase the number of commodity hardware easily and it is a fault-tolerance solution also we have discussed this one if one node goes down then another node will come into the system and when one node goes down other nodes can process the respective data can be stored as a structured unstructured and semi-structured mode.

It is more flexible in operation we know that in case of structured data we can represent our data in the rows and columns database is a good example of structured data and the sources might be the say we are having the web locks we're having the machine-generated data we're having the sensor data that is a source of the structured data we're having the unstructured data that means the data is not structured.

Example:

You can concern consider a text file PDF video images sent by the respective satellites and different machine-generated data can be the unstructured data and semi-structured data is something.

Like to some extent structured and to some extent unstructured as an example we can go for XML files JSON files and on all this type of data can be stored in the Hadoop system.

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