  Efficiently streaming high-volume data is essential for real-time data
analytics, visualization, and AI and machine learning model training. Various
streaming technologies and serialization protocols have been developed to meet
different streaming needs. Together, they perform differently across various
tasks and datasets. Therefore, when developing a streaming system, it can be
challenging to make an informed decision on the suitable combination, as we
encountered when implementing streaming for the UKAEA's MAST data or SKA's
radio astronomy data. This study addresses this gap by proposing an empirical
study of widely used data streaming technologies and serialization protocols.
We introduce an extensible and open-source software framework to benchmark
their efficiency across various performance metrics. Our findings reveal
significant performance differences and trade-offs between these technologies.
These insights can help in choosing suitable streaming and serialization
solutions for contemporary data challenges. We aim to provide the scientific
community and industry professionals with the knowledge to optimize data
streaming for better data utilization and real-time analysis.
