What is Hadoop used for | Hadoop in Big data
Big Data is a huge amount of data that is not easy for a single computer to handle. That is why from time to time improvement today’s market is full of Big Data Technologies and tools. The benefit we get from them is that they are cost-efficient and have better management for data analytical tasks.
This time we will go through the Big Data Tool “Hadoop”!
What is the purpose of Hadoop?
Hadoop is a software framework for distributed computing across computer clusters. It is a big data operating system. Optimized for parallel computing with structured and unstructured data and utilizing low hardware costs.
Moreover, Hadoop is not a replacement for relational databases or online transactions. That makes use of structured data. It can process unstructured data, which accounts for more than 80% of the world’s data.
It was originally developed at Yahoo! to address their need to store petabytes of unstructured data. Hadoop is a free open-source project released under Apache License 2.0. It offers huge data storage. It also offers incredibly huge processing power, and the capability to handle virtually infinite simultaneous tasks or jobs.
The basic idea behind Hadoop is to distribute the processing of data over many computers instead of having them processed on a single node. In addition, Hadoop provides fault tolerance and high scalability.
The Hadoop Based solutions or other analytical software is accessible, applicable, and quick to execute.
That is why The New York Times articles from 1851 to 1922. Totaling 11 million pages and 4TB of data, were converted to a Hadoop cluster on Amazon’s AWS at a low cost by a single employee in just 24 hours.
Major Hadoop architecture and its components
Hadoop Cluster consists of three major components:
Hadoop Distributed File System
HDFS stores files across different machines. Each machine has its own copy of the file. If any machine fails, You can access this file from other machines.
MapReduce is a programming model for parallelizing jobs across large datasets. It works by breaking down the dataset into smaller pieces called mappers and reducers. Mappers work independently on each piece of the dataset, while reducers combine the results together.
YARN is a resource manager for Hadoop. It manages the allocation of resources among applications running on Hadoop.
Importance of Hadoop
There are a number of reasons why Hadoop is an important big data technology.
- Firstly, Hadoop was designed to handle very large data sets, which is a key requirement for many big data applications.
- Secondly, Hadoop is highly scalable, meaning that it can easily be expanded to handle even larger data sets if necessary.
- Finally, Hadoop is an open-source technology. This means that it is available to anyone who wants to use it. There is a large and active community of developers who are constantly improving and extending it.
What is Hadoop and Spark
Hadoop Vs Spark
In 2012 a new project was developed which was Spark. It’s a high-level Apache project zeroed in on handling information lined up across a group. However, the greatest contrast is that it works in memory.
As the basics are clear now let’s move toward Hadoop Vs Spark
- It is an open source software framework which is used to utilize a MapReduce algorithm.
- MapReduce models of Hadoop read and compose from the work area hence dialing back the handling speed.
- It is cost-effective because it is cheaper compared to the rest of them.
- The Algorithm that gets utilized in Hadoop is PageRank.
- GraphX gets utilized by a spark.
- Spark can deal with ongoing information, from continuous occasions like Twitter, Facebook
- It handles the real-time data.
- Flash is a lightning-quick bunch of registering innovation. Which stretches out the MapReduce model to utilize more sorts of computations proficiently.
In a Nutshell
Hadoop is one of the Big Data Tools. That is an open-source, Java-based framework for processing and storing large amounts of data and The data is stored on limited commodity hardware that operates in groupings. Its distributed file system allows for parallel processing as well as high availability. In the above information, you can get all the answers which are important for you to know. Such as what Hadoop is the purpose of it, the Hadoop cluster, Hadoop Vs Spark, its components, etc.
If you still have any questions you can definitely ask and we will provide you with the desired answer to your question!