Today, Hadoop is widely used technology for storing and processing big data. Hadoop’s specific use cases comprises of data searching, data storage, data processing, data analysis, large-scale indexing of files, data reporting and other processing activities using what’s popularly called in the IT space as “Big Data”. Hadoop technology offers data processing and data storage through commodity hardware, which is relatively inexpensive when compared to its alternatives in the marketplace. In fact, more than half of the fortune 500 companies are using Hadoop software.
Let us know, when to use and when not use the hadoop technology:
When To Use Hadoop:
❖ Processing Seriously Big Data: If your data is really big in size, means to say not in gigabytes, but in terabytes or petabytes, then hadoop is the right choice to process your real big data. There are many alternatives to hadoop like MongoDB, SQL, RDBMS, Spark and other NoSQL database options in the market but hadoop is available with a low-cost of adoption and maintenance. When implemented hadoop, you may not have large data sets but gradually it may increase for various market reasons, so a careful and perfect planning can be set for future point of view. Hadoop Online training covers the basic functionalities of Hadoop as a big data platform.
❖ Varied Set of Data: Hadoop can store and process massive volumes of diversified file data- big or small, could be plain text files or binary files such as images or even various distinct versions of data formats throughout different stages of processing time. At any point of time, you can alter the way you process and analyze your hadoop data. So, while processing massive volumes of different data types you can do innovative experiments with its flexible strategies which will be helpful in bringing an effective solution at the end. The title given for these flexible data types is referred as Data Lakes.
❖ Parallel Data Processing: For performing this, what comes into the field is MapReduce, a programming model and an inbuilt implementation framework for processing and creating big data sets with a parallel and distributed algorithm on a cluster. MapReduce algorithm enables you to execute parallel data processing across massive data sets by using a large number of nodes or computers.
When Not To Use Hadoop:
❖ General Network file System: Hadoop Distributed File System(HDFS) lacks many of the standard POSIX(Portable Operating System Interface) file system characteristics that softwares expect from a general network file system. As per hadoop documentation, Hadoop Distributed File System frameworks requires a write-once-read-many access pattern for files. Once a file is created, written and closed should not be altered except for truncates and appends.
❖ Relational Database: Hadoop cannot be used for relational database systems because of its slow response times.A possible remedy for this problem is to go for Hive SQL engine. Hive solution offers data summaries and provides ad-hoc querying.
❖ For Real-Time Data Analysis: Hadoop functions as a batch system, processing long-running jobs on large-data sets. When compared with some relational databases, these jobs consumes long time to process on some tables. It is routine that processing of hadoop jobs is a time taking process and will take hours and sometimes days to complete the processing and specifically when dealing with seriously large data sets.
Tekslate Hadoop training covers the realtime implementations of Hadoop.