Hadoop is an open source scalable framework written in Java. It is a platform for large data storage and processing. Hadoop provides parallel processing of data as it works on multiple machines simultaneously. It provides an efficient framework for running jobs on multiple nodes of clusters. Hadoop is for processing huge volume of data. It can store the files in the range of GBs to TBs. Commodity hardware is the low-end hardware, they are cheap devices which are very economical.
Hadoop works in the master-slave model. Master manages, maintains and monitors the slaves while slaves are the actual worker nodes. Master stores the meta-data and slaves are the nodes which store the data. The client connects with master node to perform any task. Hadoop uses technologies such as map-reduce programming and Hadoop distributed file system.
MapReduce is the processing layer of Hadoop. It is a programming model designed for processing large volumes of data in parallel by dividing the work into a set of independent tasks. Hadoop distributed file system which provides storage in Hadoop in a distributed model. In this architecture, a daemon called name node run for HDFS in master node and data node on slaves. Hence slaves are also called as data node. Namenode stores meta-data and manages the data nodes. Datanodes stores the data and does the actual task.
Apache Hadoop is the most popular and powerful big data tool. Here are some of the features of Hadoop
Apache Hadoop is an open source project. It means its code can be modified according to business requirements.
As data is stored in a distributed manner in HDFS across the cluster, data is processed in parallel on a cluster of nodes.
Fault tolerant is one of the main features of Hadoop. Three replicas of each block are stored across the cluster. So if any node goes down, data on that node can be recovered from other nodes easily.
Data is reliably stored on the cluster of machine despite machine failures. If your machine goes down, then also your data will be stored.
Data is highly available and accessible despite hardware failure due to multiple copies of data. Even if the system crashes the data can be accessed by another way.
Hadoop works on data locality principle which states that move computation to data instead of data to computation. When a client submits the MapReduce algorithm, this algorithm is moved to data in the cluster rather than bringing data to the location where the algorithm is submitted and then processing it.
Hadoop is highly scalable that the new hardware can be easily added to the nodes. It provides horizontal scalability which means new nodes can be added on the fly without any downtime.
Hadoop generates cost benefits by bringing massively parallel computing to commodity servers, resulting in a substantial reduction in the cost per terabyte of storage, which in turn makes it reasonable to model all your data.
One of the most important factors to consider when choosing a commercial Hadoop distribution is based on your requirements. You can then choose depending on the set of features and specs that best suits your requirements and solves your specific business problems. Here we provide you with the list of most popular Hadoop distributors suitable for your business.