Best Ways of Using Hadoop to Manage the Huge Data Sets
Hadoop is a data management platform which helps in managing large data. Hadoop big data analytics tool has been designed in such a way that it has become one of a part of big data distributions. Most of the internet companies adopt Hadoop with its latest technology and use it for the overall development.
Hadoop architecture has several vendors package tools which are combined in commercial Hadoop distributions. That is the main reason Facebook, twitter all have taken up Hadoop for its development.
For getting high-performance Hadoop big data analytics tool is the first choice for most of the users. They establish Hadoop for managing big data. Hadoop is also cost-effective and support developed analytics initiatives.
User can use Hadoop for both reporting and analytical applications. From traditional structured data or unstructured data and semistructured data, Hadoop manages everything efficiently.
Core Components of Hadoop
Hadoop has a variety of open source software components which helps in capturing, processing, managing and analyzing a huge amount of data with different technologies.
- Hadoop HDFS– This file system helps in distributing files to storage nodes in a Hadoop cluster.
- YARN– It distributes the resources of cluster to running applications as well as tracks on the overall process.
- MapReduce– This component is used for batch application.
- Hadoop Common– Other components take advantage of this stuff and use for their improvement.
- Hadoop Ozone and Hadoop Submarine– This component is full of new technologies. User can use it as an object store and learning machine.
These are the top software modules layer for managing data storage hardware nodes. You can connect all these nodes with an internal network to build a performance and distributed based processing system.
Importance of Hadoop In Managing Large Scale of Data
Hadoop big data analytics tool is a powerful data management system which has strong features. It gives you the world’s most reliable storage layer (HDFS), distributed processing layer (MaoReduce) and resource management layer (YARN). In this article you are going to get the best features of Hadoop which manages big data distribution.
Work On Data Locality
Hadoop can move the computation close to where the actual data resides on the node. Thus reduces the network completion and enhances the overall process.
Competent to Use
For dealing with distributed computing, Hadoop does not require of any client. Everything is taken care of by Hadoop’s framework. Overall you can easily use it for managing data.
Flexibility
All kinds of data can be easily manageable because of the flexibility of Hadoop. It can deal with all types of data- structured, unstructured or semi-structured.
Cost-Effective
Because Hadoop can be driven on the cluster of commodity hardware, it is very cost effective. For using low-cost commodity hardware, you do not have to expend a lot of money to scale out your Hadoop cluster.
Compatibility
Hadoop has high availability features that is why it can copy data multiple times. You can easily access to all data without facing any hardware failure. If your system will face any issue you can retrieve your data without fail by using another path.
Credibility
Hadoop can replicate data and that is why you can store your data on the cluster of machines without hesitation. If any of the nodes fails, you can rely on Hadoop for storing data
Open Source
Hadoop’s framework based on java programming language. Because it is an open source tool, you can use it freely.The best part is that you can change its source whenever it is required.
Scalability
User can easily add new nodes to Hadoop as it is a scalable platform. Even you will not face any downtime while adding new nodes. For its horizontal scalability you can add new node on the fly model to the system. Numerous nodes can be run on Apache hadoop.
Process of Data Distribution
You can store a large amount of data in Hadoop. All these data are stored in a distributed manner in HDFS by Hadoop and processes in parallel on a cluster nodes.
Error Tolerance
Hadoop big data analytics tool creates replica and by this process of creating replica it controls all the faults or errors. During the time of storing files in HDFS, these files are being divided by Hadoop into blocks. Then these data blocks will be distributed to other machines present in HDFS cluster by client.During this process if any machines fail to perform still these data are easily accessible from other machines.
Final Words
Overall Hadoop is capable for tolerating high faults. It is a reliable tool for storing huge data without getting any hardware failure. You can get high scalability and high availability from Hadoop.
Because it is not expensive, you can run it on a cluster of commodity hardware. Hadoop can be used as moving computation which is cheaper than moving data. For having all these powerful features Hadoop is a great choice for managing a large amount of data.