In the era of big data, organizations are faced with the daunting task of efficiently processing vast amounts of data to extract valuable insights. Traditional databases and data processing systems often struggle to cope with the scale and complexity of these datasets. To address this challenge, parallel query processing has emerged as a key technique, enabling the distribution of query workloads across multiple computing resources. This abstract explores the architectures and performance considerations associated with parallel query processing for big data. Architectures: MPP (Massively Parallel Processing) Databases: Many big data systems leverage MPP databases, which distribute data across multiple nodes and employ parallelism to execute queries efficiently. We delve into the principles underlying MPP databases and discuss how they partition and parallelize data for rapid query execution. Hadoop MapReduce: The MapReduce programming model is widely used in big data processing. We examine how MapReduce divides tasks into map and reduce phases, leveraging parallelism to process data efficiently. Additionally, we discuss the Hadoop ecosystem, including tools like Hive and Pig, which simplify query processing on Hadoop clusters. This abstract serves as an introduction to the complex and evolving field of parallel query processing for big data. The architectural insights and performance considerations discussed here are crucial for organizations seeking to harness the power of big data analytics while optimizing query performance.
Keywords: Apache Spark, Big Data, Data Distribution, Hadoop MapReduce, MPP Databases, Parallel Query Processing
