The most ambitious project of India, Aadhaar project relies completely on Big Data. From collection of data to storage and utilization of biometric information of the entire population, big data till date has crossed over the billion mark. It is needless to say, a project of such a vast magnitude must be plagued with ample challenges but as its powered by big data, the chances of success is high.
Basically, Aadhaar is a unique 12 digit number assigned by the UIDA, Unique Identification Authority of India to an individual residing in India. The project was launched in the year 2009 under the supreme guidance of former Infosys CEO and co-founder Nandan Nilekani. He was the sole architect of this grand project, which required several added inputs from various other sources.
MapR, a business software company headquartered in California is providing technology support for the most-ambitious Aadhaar project. It is the developer-cum-distributer of “Apache APA +0.00% Hadoop” and for quite some time it is optimizing its well-integrated web-scale enterprise storage and real-time database tech for this project.
The encompassing technology architecture behind Aadhaar is structured on the principles of openness, strong security, linear scalability and vendor neutrality. The system is expected to expand with every new enrollment, which means it’s required to handle millions of transactions through billions of records, each day.
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Do you know why several organizations face problems while implementing Big Data? Still wondering? The reason is lack of poor or non-existent data management strategies.
Proper technology systems need to be adopted. Without procedural flows, data is impossible to be analysed or delivered appropriately. However, before we delve deeper into making a plan to introduce data management strategies into the business, we should pay enough attention to the systems and technologies we are thinking to launch, along with the number of improvements to be made.
Big Data is ruling the tech world. Here are few types of tech that needs to be a part of a successful data management strategy:
Common data mining tools are R, SAS and KXEN.
More consistent, Automated ETL is used to extract, transform and load data.
They are efficient in offering a protective layer of security and quality assurance by doing a proper problem diagnosis and monitoring critical environments.
BI and Reporting Analytics
Turn data into insights, with BI and Reporting Analytics. It is very vital that data go to the right people and of course in the right manner. If that doesn’t happen, organizations suffer incessantly.
Analytics is a huge branch of study, starting from customer acquisition data, tracking details to intriguing user-friendly interfaces and product life cycle.
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Understanding The Core Components of Data Management
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