Efficient Query Processing Techniques for Big Data Analytics (Published)
In the era of big data, organizations are inundated with vast volumes of data from diverse sources. To extract meaningful insights and drive informed decisions, efficient query processing techniques are essential. This abstract provides an overview of the challenges associated with big data analytics and introduces various techniques that have been developed to address them. Big data analytics necessitates the processing of massive datasets, often characterized by three Vs: volume, velocity, and variety. Traditional database management systems are ill-equipped to handle such data due to their limited scalability and processing capabilities. As a result, novel approaches are required to enable efficient query processing in the context of big data. This abstract discusses the key challenges faced in big data analytics, including data storage, data retrieval, and data processing. To address these challenges, several techniques have been developed. These techniques encompass distributed data storage, parallel processing, and data indexing. The utilization of distributed storage systems like Hadoop Distributed File System (HDFS) and NoSQL databases allows for efficient storage of large datasets. Parallel processing frameworks, such as Apache Spark, enable the simultaneous execution of queries across distributed clusters, significantly improving query performance. Data indexing, whether using traditional B-tree indexes or specialized index structures like columnar databases, enhances query retrieval speed by minimizing data scanning. The abstract also highlights the importance of machine learning and artificial intelligence techniques in big data analytics. Machine learning algorithms, such as deep learning and natural language processing, facilitate predictive analytics and sentiment analysis on big data, enabling organizations to gain valuable insights from unstructured data sources.
Keywords: Big Data Analytics, Data Processing, Data analysis, Distributed Computing, Efficient Query, Query Processing, query optimization