Data virtualization in the age of Big Data

Big Data is undeniably a major technological leap and the buzzword of the decade. Despite its emergence in the early phase of current digital era, big data is still a consistent part of crucial developments in the field of data management and study.

At its core, Big data is nothing but a collection of raw data, structured or unstructured, stored in huge volumes and metrics that are obtained from distinctive data sources like operations, user database, etc. This huge amount of data is then evaluated using an ingenious analytics engine that delivers meaningful insights and patterns using available data sets. Big data analytics requires immense computational power and logic implementation to extract the probable insights and patterns that can procure useful conclusions for any organization. Substantial development in Big data applications fostered the rise of advanced data analytics that helped organizations devise actionable insights from proprietary data on hand. Key players in the tech market are profoundly putting advanced data analytics at use to track remote business possibilities to reap higher revenues and market reach.

Even with its countless application areas and business benefits, data analytics remains a distant thought for implementation in mainstream organizations. Even if obvious implementation issues like security and data loss are mitigated, the complexity of data feed still persists. Data fed into the analytics engine lacks categorization, which raises operational complexity to multi folds.

With the advancement in data management technologies, data virtualization founds its utility in the big data application. Data virtualization effectively resolves standard issues in big data analytics by largely simplifying data sets and its suitability for analytical evaluation. Data virtualization is an advanced abstraction technique that delivers appropriate data sets without complex formatting related to type of data or storage point. Data virtualization or DV streamlines data from an assorted range of sources into single comprehensive view, allowing for a generalized data access for analytical use. Unlike popular belief, data virtualization does not change or manipulate data. It delivers an integrated view of mission critical data at run-time, which can be used for big data analysis and derive important conclusions. Data virtualization provides unified data sets, virtually integrated to support real-time data evaluation, at lower cost and higher efficiency. It is a modern day agile implementation of traditional integration process without the need of distinctive infrastructure and IT components.

Data virtualization is highly applicable in large enterprise and SMEs to aid extensive data analytics with great agility and efficiency. PSI-Globaltech’s data analytics and BI solutions employ exhaustive analytic approach to provide our clients with comprehensive insights about their business process and management. Our BI models are constructed to analyze the slightest of possibility regarding business variables. Read more about out services at www.PSI-Globaltechtteam.com