In-Memory Query Processing for Big Data: Speeding up Insights (Published)
In the era of Big Data, the volume, velocity, and variety of data have challenged traditional relational database systems. NoSQL databases have emerged as a compelling alternative, designed to handle massive datasets and offer flexible data modeling options. This paper provides a comparative analysis of NoSQL databases in the context of Big Data query processing. The primary objective of this study is to evaluate the performance, scalability, and suitability of various NoSQL database types, including document-based, column-family, key-value, and graph databases, in handling the unique demands of Big Data workloads. We explore the advantages and limitations of each database category concerning schema flexibility, data consistency, and query execution. Additionally, the paper investigates key Big Data query processing techniques and their compatibility with NoSQL databases. We analyze how distributed processing frameworks like Hadoop and Spark interact with NoSQL databases, emphasizing their integration, efficiency, and query optimization. To achieve these objectives, we conduct a comprehensive review of recent research, industry trends, and practical use cases involving NoSQL databases and Big Data applications. We also present performance benchmarks and use cases to showcase the strengths and weaknesses of NoSQL databases when employed in various Big Data scenarios.
Keywords: Big Data, NoSQL databases, Performance Evaluation, data modeling., database types, distributed processing, query processing, scalability
NoSQL Databases and Big Data Query Processing: Comparative Analysis (Published)
In the era of Big Data, the volume, velocity, and variety of data have challenged traditional relational database systems. NoSQL databases have emerged as a compelling alternative, designed to handle massive datasets and offer flexible data modeling options. This paper provides a comparative analysis of NoSQL databases in the context of Big Data query processing. The primary objective of this study is to evaluate the performance, scalability, and suitability of various NoSQL database types, including document-based, column-family, key-value, and graph databases, in handling the unique demands of Big Data workloads. We explore the advantages and limitations of each database category concerning schema flexibility, data consistency, and query execution. Additionally, the paper investigates key Big Data query processing techniques and their compatibility with NoSQL databases. We analyze how distributed processing frameworks like Hadoop and Spark interact with NoSQL databases, emphasizing their integration, efficiency, and query optimization. To achieve these objectives, we conduct a comprehensive review of recent research, industry trends, and practical use cases involving NoSQL databases and Big Data applications. We also present performance benchmarks and use cases to showcase the strengths and weaknesses of NoSQL databases when employed in various Big Data scenarios.
Keywords: Big Data, NoSQL databases, Performance Evaluation, data modeling., database types, distributed processing, query processing, scalability