Apache Hadoop is an open-source platform for distributed computing. It is more of a framework for processing of clusters of data sets using different programming models. It is designed to scale up from single to multiple machines using local computation and storage. It is known for reliability and scalability, and it detects failures and rectifies them at the application layer. Therefore, it delivers services with higher availability, especially when handling clusters of computers.
Benefits Of Apache Hadoop –
Hadoop is used especially to store, manage, and analyze structured and unstructured data, and it is very popular among various big organizations. The following is the list of benefits Apache Hadoop has to offer.
Reliability – The large the number of computer clusters, the higher is the rate of failure in individual nodes. Hadoop is designed to cope with the failure in individual nodes. When an individual node fails, it redirects to the remaining nodes for the process to continue, and the data is replicated for handling future failures.
Flexibility – There is no need to create structured schemas as it is the case with traditional RDBMS. That is why the organizations are preferring tools like Hadoop, where they can store data in any format. In the case of semi-structured and unstructured format, the parsing and applying schema takes place when reading the data.
Scalability – In data storage, management, and analysis, scalability is a major factor. Due to the unique approach of Hadoop, it can handle the scalability factor without compromising with the performance.
Costing – There are many proprietary software platforms available, and that is why they cost more. Since Hadoop is an open-source platform, the costing is way lower in all aspects.
What Are Its Applications and What Are Not?
Processing Big Data – Apache Hadoop is used for processing big data. This big data is not just a large chunk of data, it is meant for really big data on the scale of petabytes and not just terabytes. There are plenty of tools available for gigabytes and terabytes of data with a lower cost of maintenance. You can start with other tools, and if data grows exponentially, you have to finally opt for Apache Hadoop.
Storing Diverse Data – Apache Hadoop is tailor-made for storing various types of data in different formats. It can be text data, images, and much more. You can analyze those data after processing, and you can change the way the processing and analyze at any given moment. There is no hard and fast rule, which is why it can store a diverse set of data in the best possible way.
Parallel Processing – Hadoop uses the exclusive MapReduce algorithm that lets it do parallel data processing. There is always a need for processing variables jointly, and the MapReduce algorithm lets Hadoop do that easily.
However, there are a lot of areas where Hadoop does not fit it, such as the graph-based data. That is why there is a separate product named Apache Tez. Apart from that, the real-time data analysis is not up to the market. Besides, you should use an RDBMS when there is retaliation data to store.