Data is exploding. Unstructured data is growing faster than structured data. Big data is the buzz everywhere. Big data refers to huge data sets characterized by larger volumes, greater variety and complexity, generated at a higher velocity than any organization has faced before.
To make meaningful analysis of exploding and unstructured data, a technology enabled strategy is required for gaining insights into the data which ultimately helps in gaining competitive advantage. Organizations can optimize success only if it weaves analytics into big data solution. Industries such as Manufacturing, Retail, Banking & Finance, Airlines, Health care provider, Telecommunication etc are in great need of Big Data Analytics for their unstructured data sources. The data sources from these industries may typically fall under the following categories: Transaction Data, Machine Generated Data, Human Generated Data, Biometric data, Web and Social media data. Big Data analytics are driven by use cases that are specific to given industry. The challenge is, traditional analytics IT infrastructure is not able to meet the demands of Big Data Analytics landscape.
Technologies have emerged to make easy and cost effective analysis of unstructured data as unstructured data sources used for big data analysis may not fit in traditional data warehouses. Big data analytics is the application of advanced analytic techniques to very large, diverse data sets. There are advanced analytics techniques like predictive analysis, data mining, statistics, etc to study the state of business and data. NoSQL, Hadoop and Mapreduce are new methods of working with big data that offer alternatives to traditional data warehousing. These technologies form the core framework that supports the processing of large data sets across clustered systems.
The evolving best approach to unstructured data analytics is Apache Hadoop. Hadoop is an open-source framework that uses a simple programming model to enable distributed processing of large data sets on clusters of computers. Hadoop is the Nucleus of the next-generation big data massively parallel processing enterprise data warehouse. Hadoop is more cost-effective for handling large unstructured data sets than conventional approaches, and it offers massive scalability and speed. The complete technology stack includes common utilities, a distributed file system, analytics and data storage platforms, and an application layer that manages distributed processing, parallel computation, workflow, and configuration management. In addition to offering high availability, it offers massive scalability and speed.
MapReduce is a programming model and software framework for writing applications that rapidly process vast amounts of data in parallel on large clusters of compute nodes. MapReduce is a framework for writing applications in Hadoop and MapReduce job consists of 2 phases: Map and Reduce. MapReduce takes advantage of the parallel processing power of distributed systems.
Finally, a technology is now available for organizations struggling with unstructured data where the analysis is challenging and affecting their strategic decision making. Big Data Analytics- On the Move!!