Big data processing systems (eg, Hadoop, Spark, Storm) contain a vast number of
configuration parameters controlling parallelism, I/O behavior, memory settings, and …

Distributed data stream processing systems (DSPSs) such as Storm, Flink, and Spark
Streaming are now routinely used to process continuous data streams in (near) real-time …

The serverless execution paradigm is becoming an increasingly popular option when
workloads are to be deployed in an abstracted way, more specifically, without specifying any …

The growing need to identify patterns in data and automate decisions based on them in near-
real time, has stimulated the development of new machine learning (ML) applications …

Abstract [eng] This work consists of literature analysis and research. The literature part
examines the workings of stream processing systems, way to measure their speed and the …

Current Big Data applications have significantly evolved from its origins, moving from mostly
batch workloads to more complex ones that may involve many processing stages using …

An increasing number of real-world applications today consume massive amounts of data in
real-time to produce up to date results. These applications include social media sites that …