Big data management using apache spark: Analysis of bank customer s who are interested to maintain an account based on their age group

Joint Event on 7^th International Conference on Biostatistics and Bioinformatics & 7^th International Conference on Big Data Analytics & Data Mining

September 26-27, 2018 | Chicago, USA

M Suneetha

Rayalaseema University, India

Posters & Accepted Abstracts: J Comput Sci Syst Biol

Abstract :

Due to some unavoidable situations, customers are not interested to save their money in Banks. Nowadays all the ATMs are run out of money problem. Many customers are taking back their investments and Deposits from the bank. If this situation continues the Banking sector may not survive in the future. We have done marketing Campaigns based on phone calls in Bangalore. Huge data collected from the banks and Data analysis algorithm is used to manage the Data in the Apache Spark environment. Marketing Campaigns are very important to analyze the marketing trends to withstand the failures and to move towards a progressive approach. In this research, we collected huge data from the banks. Another burning problem is Data Management. Day by day data is growing like a Giant. Data is generating in every second from every corner of the world. In this research, we concentrated on data analysis and managing data by using Data analysis algorithm in the Apache Spark using map reduce. Spark is a Big Data Framework and is an open source. It is a high-speed general-purpose data processing engine. For fast computations, this is the best choice. It also covers a wide range of data processing methods like batch processing, Interactive, Iterative and stream processing of Data. Comparatively, Spark framework is better than Hadoop Framework. Spark is a better alternative for streaming workloads, machine learning and interactive queries. Hadoop modules can work with Spark. Spark is a top choice for Big Data Analytics for real-time data processing. A lot of disks read and write time is saved by using Spark. Spark Resilient Distributed Dataset allows to store data in-memory and send to disk only whatever important or needed. Spark can process real-time data, such as Twitter and Facebook data. But Map-reduce fails when it comes to real time data processing, but Spark can efficiently manage it.

Biography :

E-mail: suni.mulumudi@gmail.com