What Makes Apache Hadoop The Most Popular Big Data Framework?

Apache Hadoop is often considered the most powerful framework for big data. It was revolutionary when it first came out with its all-encompassing solution for Big Data storage and processing, so much so that now it is almost synonymous with Big Data. Big Data software is mostly built around Hadoop or is compliant with it. It is also known for its reliability due to its HDFS storage layer and YARN.

Here are some reasons why it is so popular:Hadoop's architecture is made primarily of two elements- Hadoop Distributed File System (HDFS) and Yet Another Resource Negotiator (YARN). While HDFS takes care of big data storage, YARN handles all the processing tasks by allocating resources and scheduling job mechanisms. The two components work collaboratively and are then divided into further components for delivering solutions that are better and more enriched. Hadoop's architecture is therefore superior to most other in the category.

Hadoop is simple to use. It can be the best solution in case of data processed in batches, and that is made into smaller processing jobs. If the data is spread across a cluster, they can also be managed easily with Hadoop, and it is not always necessary for the data to be complex.

Hadoop is open-source. It can be accessed for free, and at the same time, Hadoop developers can modify its code as per the requirements of the enterprise. It does not require extra cost to do so. Further, Hadoop runs on a cluster of hardware which makes it highly cost-effective.

In Hadoop, data processing is done parallelly using a cluster of nodes. Due to this, it is easier to distribute data. Hadoop also has a high rate of fault tolerance. For each block, three replicas are stored across the cluster by default. If required, this can also be changed. In case any node is faulty, the other nodes can be used to recover the data on that node. The framework is able to recover lost nodes and tasks automatically. Due to this, Hadoop is highly reliable.Further, storing data on Hadoop is so reliable. Even when the machine is down, it will store the data and replicate it in the cluster to ensure that there are multiple copies of it available for use. Once stored, this data is easily accessible either directly or by using another path in case the machine stops working or there are some technical glitches.

Scalability is another feature of Hadoop that makes it so easy to use. It is highly scalable. It is easy to add new hardware and nodes whenever required, and there is no downtime involved in doing so.

Hadoop originally began as a MapReduce algorithm. Therefore there are many tools in its ecosystem such as YARN for supporting it. YARN is primarily used in resource management, and its level of functionality is such that it can also be used in other Apache frameworks such as Apache Spark. Further, Hadoop works on the principle of data locality. It simply means that when someone submits the MapReduce algorithm, it is moved to the data in the cluster. The data is not transferred to computation, and this helps to process data at a much faster pace.

A major concern in the case of data storage is security. With Hadoop, this concern is much less. It supports Kerberos authentication and third-party authentication. It also supports access control lists, conventional file permissions, and so on. Its security results are, therefore, highly improved. Hadoop's usability is such that it is popular even among startups. It is the best hardware of analysing archive data and can be used for other purposes as well such as financial trading and forecasting, executing commodity hardware operations, linear data processing, and so on. When it comes to customer analytics, there is no better framework than Hadoop. Despite the growth of cloud computing and the advent of other frameworks in the market, Hadoop is likely to remain popular for years.

Also Read - How should you use big data and cloud computing to enhance your business.