Organizations need databases which are needed to store data safely. Through databases one can solve problems from NoSQL and RDBMS framework to in-memory databases. This help banks to give quicker reaction times and viable examination, prompting better client experience and maintenance. Utilizing a center layer on top of various databases, banks can rapidly assemble information. For more read the article written by Nanda Kumar(CEO, SunTec Business Solution) : https://www.finextra.com/blogposting/12478/making-data-work-for-banks

Today's databases should be flexible and should be able to deliver extreme performance and handle humongous data volumes. So database architects have come up with NoSQL, NewSQL alternatives to relational database management systems (RDBMS). In order to choose among these three, there has to be a fundamental understanding of all the three technologies. RDBMS can handle thousands of transactions per second but the new face of online transaction processing (OLTP) in scenarios such as real-time advertising, fraud detection, multi-player games, and risk analysis, to name a few, involves close to a million transactions per second -- a pace that traditional RDBMS has problem in dealing with. These problems can be addressed by NoSQL and NewSQL. NoSQL database management systems store data in a variety of formats. Most NoSQL products discard ACID performance to achieve data storage flexibility. NewSQL, retain both SQL and ACID, but they overcome the performance overhead of RDBMS. In order to choose the type of database the following questions have to be answered-To what extent do you rely on data in terms of storage, processing, and analysis? How important are the scale, flexibility, and performance aspects of a DBMS? What is your level of investment in incumbent technologies? Read more at:

http://www.informationweek.in/informationweek/news-analysis/297462/choose-nosql-newsql-rdbms

One can use the big data, available in hand, in a right or a wrong way. Here is the list of top 10 worst big data practices which one should try to avoid. First, though MongoDB has an aggregation platform, it is not good as an analytical system and thus should not be used as big data platform. Second, RDBMS schema is used as files by many which should be avoided too. Third, creating a series of data points. Fourth, failing to develop use cases. Fifth, over-dependence on Hive should be reduced as the whole point of big data is to expand beyond what one could do with one technology. Sixth, it's not right to treat HBase like an RDBMS. Seventh, trying to install Hadoop and all its moving parts on 100 nodes by hands is also a worst practice. Eighth, one should also avoid RAID/LVM/SAN/VM-ing one's data nodes. Ninth, instead of treating HDFS as just a file system one needs to think about how one is going to secure all of this and for whom. Finally, everyone is free but each one should have a plan. Read more at:http://analytics.theiegroup.com/article/53c925453723a81857000073/The-10-Worst-Big-Data-Practices-