Toward salable and efficient NoSQL Query Processing by using Big Data

Data-driven applications have grown increasingly dependent on the effective management and retrieval of great amounts of semi-structured and unstructured information in the Big Data age. Offering scalability, flexibility as well as great availability. NoSQL databases have become a common substitute for conventional relational databases, due to their schemeless character and varied data types. NoSQL systems create a major complex problem in improving query speed. This study investigates several methods and approaches to enhance query performance for NoSQL databases that are particularly designed for Big Data applications. Documents, key-value, column family & graph databases are few of the NoSQL models addressed along with query planning, caching, denormalization, data partitioning & indexing strategies. Experimental evaluations and simulations are presented, demonstrating the impact of these methodologies on real data sets. The results provide guidance on best practices for data engineers and developers hoping to create scalable, high-performance Big Data systems using NoSQL technology.

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