Why wait for sequential processing when parallel is faster?

Sequential and parallel processing are two distinct methods of executing software tasks, with sequential processing handling tasks one at a time and parallel processing executing multiple tasks simultaneously. While sequential processing was once the norm in general-purpose computing, parallel processing has become essential for handling large-scale data analytics due to its ability to divide tasks into smaller units and distribute them across multiple processors, significantly reducing execution time. Parallel processing is especially advantageous for big data analysis, leveraging modern multi-core processors and cloud computing to manage petabyte-scale datasets efficiently. The choice between schema-on-write and schema-on-read also affects data processing; schema-on-write requires defining data structure before processing, ensuring reliability, while schema-on-read offers flexibility by allowing data structure to be defined at query time. Starburst's open data lakehouse analytics platform exemplifies the benefits of massively parallel processing by enhancing performance and cost-efficiency for diverse data consumer needs, from business intelligence to machine learning applications.