Understanding Hadoop Distributed File System And MapReduce Framework
Faster networks, inexpensive data storage, and high-performance clusters are changing the way we use computers today. One of the most prominent areas where change is evident is the paradigm for unstructured data storage. The biggest storyline to this data storage is none other than Hadoop. It is a free tool that is bringing the inaccessible vision of big data to practical problems on regular networks. In fact, large numbers of companies today are experiencing the big data revolution with Hadoop. Even smaller companies are looking forward to implementing Hadoop in the best environment.
All About Hadoop:
Hadoop is about distributed and parallel processing of data. It comprises of two main components and these are the Hadoop Distributed File System and MapReduce. Data is sorted and stored in HDFS, while MapReduce provides a simple understanding and use of the method of processing the data that is stored in HDFS. Hadoop provides a fault tolerant method of distributing multiple copies of data to a cluster. Well, replication of data is not a new idea, but HDFS works with tables and indexes of data. Its main aim is to work with unstructured, unsorted and unindexed files of data that helps in dealing with wide varieties of complications, as a whole.
Analyzing The Data:
It is a well-known fact that only storage of data is not the ultimate solution. There is often need to analyze data to serve different purposes. It is here that MapReduce comes into play. The main task of MapReduce is to analyze the data on HDFS, and run over most of the data on the cluster. Hence, the clusters of Hadoop are highly scalable because everything occurs parallel. As there are more machines in a cluster, the storage capabilities are increased without creating any impact on the total time taken to process the data.
Distributed File Systems:
HDFS is often considered to be similar to many other distributed file systems, but it is different from the others in large numbers of ways.
- It simplifies the coherency of data,
- Promotes high accessibility of large numbers of materials,
- It helps in locating processing logic near data instead of moving the data to the application space,
- The model is simple, robust and coherent,
- Processes large amounts of data reliably,
- It provides an economic option for storage and distribution by processing across clusters of personal computers,
- It distributes the data logic for processing thereby increasing the efficiency parallel on nodes,
- It provides interface for applications to move on closer to the source of data.
The framework of HDFS and MapReduce is distributed automatically. These can be installed on standard hardware with excellent scaling characteristics. This in turn makes it possible to run a cost-effective cluster that can be dynamically extended with the purchase of more computers. Moreover, the effort of operating can also be reduced by getting access to cloud capacity. Therefore, larger numbers of business organizations are making use of the Hadoop technology and the frameworks within the system in order to make sure that they get the most efficient and cost-effective solution.