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Big Data – Source, Management and Uses – Part II

Having seen what Big Data is, this article briefly discusses sources of Big Data, the handling and management of Big Data and the future perspectives estimated with Big Data.

Sources of Big Data

Undoubtedly Big Data is a concern in all dimensions - volume, velocity, and variety. Before attempting to handle the data, it's important to know the sources of Big Data to get an idea of where and how to start.

Streaming data – Data received from a network of connected devices. This data can be analyzed upon arrival and can be segregated into data that need to be kept, data that can be ignored and data that needs further analysis.

Data from social media – Data that floods in from social medias like Twitter, Facebook, Pinterest, etc., contains attractive information that could be used mainly for sales, marketing and support activities. Usually, data from social media is in unstructured format. Sometimes, semi-structured data will also be available from social media. This poses a greater challenge in acquiring and analyzing this data.

Public data domains – Open data sources are available to get Big Data and some of the sources are US government portal data.gov, European Union Open Data Portal, CIA World Factbook, US Census Bureau( the information database of US citizens), data.gov.uk(the UK government data), and so on.

These sources are a very few from the enormous sources available for Big Data.

Handling Big Data

Because Big Data takes too much time. It costs too much money to load into a traditional relational database for analysis. New approaches to storing and analyzing data have emerged that rely less on data schema and data quality. Instead, raw data with extended metadata is aggregated in a data lake and machine learning and artificial intelligence (AI) programs use complex algorithms to look for repeatable patterns.

Big Data analytics is often associated with cloud computing. The reason is that analysis of large data sets in real-time requires a platform like Hadoop* to store large data sets across a distributed cluster and MapReduce** to coordinate, combine and process data from multiple sources.

Big Data management

Big Data management is the organization, administration and governance of large volumes of both structured and unstructured data.

The goal of Big Data management is to ensure a high level of data quality and accessibility for business intelligence and Big Data analytics applications. Corporations, government agencies and other organizations employ Big Data management strategies to help them contend with fast-growing pools of data, typically involving many terabytes or even petabytes of information saved in a variety of file formats. Effective Big Data management helps companies locate valuable information in large sets of unstructured data and semi-structured

data from a variety of sources, including call detail records, system logs and social media sites.

Most Big Data environments go beyond relational databases and traditional data warehouse platforms to incorporate technologies that are suited to processing and storing nontransactional forms of data. The increasing focus on collecting and analyzing Big Data is shaping new platforms that combine the traditional data warehouse with Big Data systems in a logical data warehousing architecture. As part of the process, they must decide what data must be kept for compliance reasons, what can be disposed off, and what should be kept and analyzed in order to improve current business processes or provide a business with a competitive advantage. This process requires careful data classification so that ultimately, smaller sets of data can be analyzed quickly and productively.

Uses of Big Data

It's no surprise that organizations are increasingly turning to Big Data to find out new ways to improve decision making, better opportunities, and overall performances. Challenges arise every day when information is dispersed among several different systems that are not interconnected by a central system. Organizations harness the Big Data to address these challenges. Big Data helps improve decision-making capability by aggregating data across different systems.

Organizations get greater visibility into operational issues with the help of Big Data that ultimately leads to improvements in overall operations. Machine data which include computers, sensors, meters, GPS devices, etc., are highly dependable for operational insights. Shopping patterns, recommendations, purchasing behaviour and other drivers known to influence sales can be tracked and analyzed by companies since Big Data allows to do so, thus providing unprecedented insight into customers' decision-making processes.

Businesses can enhance security and intelligence analysis platforms by having access to real-time data. Intelligence, security and law enforcement insight can also be improved by processing, storing and analyzing a wider variety of data types.

Demand for Big Data

The challenge with Big Data is to analyze it and convert it into information by identifying patterns; that can be used by organizations for their growth and revenue generation. Since it has got lot of opportunities, many organizations are into it looking for solutions in how to achieve the objectives with Big Data. Big Data management is fast emerging into booming industry with major players in private industry and open source communities as developers and software providers are consistently rising to take up the challenge.

Open source providers are leading the drive in change and innovation in Big Data management while private industries are also contributing a considerable share. Apache Hadoop, Apache HBase, Cascading and MongoDB are some of the open source providers. Among the private players, SAP Sybase Q, Oracle Big Data Appliance, HP Information Optimization Solutions and IBM Big Data Platform are a few to name.

Big Data is included in the broad spectrum of Information Technology as a sub-department. Highly skilled experts in programming and data analysis are required in large numbers to extract worthwhile insights and information. Nowadays, data visualization tools are given a higher priority by the organizations to harness the power of Big Data and are despatched to the appropriate persons who can handle them wisely. To achieve wide-scale improvements in companies' future perspective, organizations convert the majority of their data into useful information by granting greater access to Big Data.

Devices and transactions that generate data streams that are increasingly complex are growing day by day rapidly and the need for effectively using that growing data is also growing exceedingly. Also, companies take this opportuniy as a competitive advantage for their growth and they are aware of the significance of this scenario. Even it is considered as an asset of great value by companies that deal with Big Data management. As a result, Big Data will obviously become bigger since organizations look for more better ways to tap into existing data, that will effectively refine the process of arriving at better decisions in crucial decision-making processes. Ultimately, answers to questions that are once considered impossible could be easily found out.

* - Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation.

** - MapReduce is a software framework that allows developers to write programs that process massive amounts of unstructured data in parallel across a distributed cluster of processors or stand-alone computers.

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