• Zulfikar Ali Ansari
• Dr. Manish Madhava Tripathi

Now a days our Generation are living with Social media Data like Heart beat, A lot of data are generating from Social media site like Facebook, Twitter, Linkdin, Youtube along with many other sources of data like CCTV data, Medical data. MRI Data Xray data, Shopping site etc. These data may be structured, unstructured and Semi structured. It is not possible of these data handling and managing in Traditional method smoothly. All these data are called Big Data. Big data is defined as large amount of data which requires new technologies and architectures so that it becomes possible to extract value from it by capturing and analysis process. Due to such large size of data it becomes very difficult to perform effective analysis using the existing traditional techniques. To handling this problem Hadoop comes in Picture. Hadoop is a framework, Hadoop is a scalable, open source, fault-tolerant Virtual Grid operating system architecture for data storage which is fault-tolerant high-bandwidth clustered storage architecture. Hadoop is an open-source framework that allows to store and process big data in a distributed environment across Thousands clusters of computers using simple programming models.Hadoop is fully dependent in a programming model or algorithm it is called Map Reduce Technique, It is also called the Heart of Hadoop Technology. Map Reduce Technique is working Parallel Processing infrastructure that runs in contact with HDFS. MapReduce is a processing technique and a program model for distributed computing based on java. Map Reducing Algorithm mainly depend on 3 Phase Map, Reduce and Shuffle. Map takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs). Secondly, reduce task, which takes the output from a map as an input and combines those data tuples into a smaller set of tuples. As the sequence of the name MapReduce implies, the reduce task is always performed after the map job and also applied another phase like Shuffle it is also must in Map Reduce Technique. Map Reducing Technique work in Chunk Form or we can say it is working thousands of machine at a time on Commodity hardware, It Contains Record Reader also it should be separate a boxes data at word count job. Record Reader is working as Interface in Map Reduce. Map Reducing is provide the functionality which is based on java Technology that’s why it is provide smoothly access Big Data. Structured, Unstructured data are storing and managing very easy from Map Reducing technique. This Paper proposed implementation of Map reducing Technology like Big Data Analysis very easily Accessing, Storing, Securing and Management. This research focusing on implementation of Map reducing Technology another field like Medical field, Shopping site, Weather climate field etc. In medical Field we can access the heart patient, cancer Patient, or any other particular patient data fetch from Big data analysis technique using map reducing technology easily. No SQL Server use in Map Reducing Algorithm, It is used Hadoop Distributed file System(HDFS)for storing and accessing data. Hadoop contains 64 MB each block which is very effective distributed file system.

"A Comprehensive Study of Map Reducing Technique in Big Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.774-779, June 2019, Available :http://www.jetir.org/papers/JETIR1906U04.pdf

"A Comprehensive Study of Map Reducing Technique in Big Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp774-779, June 2019, Available at : http://www.jetir.org/papers/JETIR1906U04.pdf