Data buckets in big data

Abstract

Big data is an evolved term for large volume of unstructured, semi-structured and structured data having the potential to be used and mined for information in machine learning projects and other advanced analytics applications. Big data is the new driver of the world societal changes and economic. The world’s data collection is reaching a tipping point for major technological changes that can bring different ways in finance, decision-making, cities, managing our health, and education. Latest technological improvements in computing, data handling, data storage, and trading have transformed the financial industry, hence growing liquidity, decreasing costs, and building new chances for business inquiries. While the data complexities are growing including data’s variety, value, velocity, volume, variability, veracity, the real impact hinges on our ability to discover the variety and scalability in the data through Big Data Analytics technologies. Due to the need of scalability as data technologies and volumes are increasing, fetching data is more time consuming, causing latency and encountering performance issues. To manage and search data, we need efficient search methodologies. Proper indexing with multiple types and enormous data is not easy with the typical indexing used in databases. Hence, the proposed solution of buckets that chunks the data by type and criteria will make content-based multimedia retrieval systems more efficient and less time consuming.