Seminar Artifacts for: Scalability Benchmarking of Kafka Streams Applications A detailed description can be found in the README.md. Abstract Stream processing frameworks have gained popularity in the past years. In contrast to the isolated analysis of individual operations we design four benchmarks for common stream processing use cases that are derived from realistic production scenarios. Therefore, we define relevant topologies that cover the realistic scenarios, scalability dimensions, metrics, and workloads that lead to our benchmark definitions. Additionally, we execute one of our benchmarks for the stream processing framework Kafka Streams. Our results show that Kafka Streams scales linearly with the workload for the given configur...
This paper describes a benchmark for stream processing frameworks allowing accurate latency benchmar...
In data streaming we work with large data from multiple sources. We observe overloaded partitions du...
International audienceBig Data applications are increasingly moving from batch-oriented execution mo...
Seminar Paper Artifacts for: Scalability Benchmarking of Kafka Streams Applications A detailed desc...
Paper Artifacts for: Collecting and Exploiting Performance Metrics of Kafka Streams Applications A ...
Paper Artifacts for: Collecting and Exploiting Performance Metrics of Kafka Streams Applications A ...
More and more use cases require fast, accurate, and reliable processing of large volumes of data. To...
Traditional databases and batch processing systems are not able to handle the loads experienced by m...
Batch processing technologies (Such as MapReduce, Hive, Pig) have matured and been widely used in th...
Modern distributed stream processing systems play an important role in cloud computing systems and B...
The constant growth of the number of Internet of Things devices drives a huge increase in data that ...
The goal of this project is to design a solution for massive mobility using LISP protocol and scala...
Real-time analysis of continuous data streams using distributed systems is an emerging class of dat...
Thesis Artifacts for: Benchmarking Scalability of Load Generator Tools A detailed description can b...
[[abstract]]Many problems, like recommendation services, website log activities, commit logs, and ev...
This paper describes a benchmark for stream processing frameworks allowing accurate latency benchmar...
In data streaming we work with large data from multiple sources. We observe overloaded partitions du...
International audienceBig Data applications are increasingly moving from batch-oriented execution mo...
Seminar Paper Artifacts for: Scalability Benchmarking of Kafka Streams Applications A detailed desc...
Paper Artifacts for: Collecting and Exploiting Performance Metrics of Kafka Streams Applications A ...
Paper Artifacts for: Collecting and Exploiting Performance Metrics of Kafka Streams Applications A ...
More and more use cases require fast, accurate, and reliable processing of large volumes of data. To...
Traditional databases and batch processing systems are not able to handle the loads experienced by m...
Batch processing technologies (Such as MapReduce, Hive, Pig) have matured and been widely used in th...
Modern distributed stream processing systems play an important role in cloud computing systems and B...
The constant growth of the number of Internet of Things devices drives a huge increase in data that ...
The goal of this project is to design a solution for massive mobility using LISP protocol and scala...
Real-time analysis of continuous data streams using distributed systems is an emerging class of dat...
Thesis Artifacts for: Benchmarking Scalability of Load Generator Tools A detailed description can b...
[[abstract]]Many problems, like recommendation services, website log activities, commit logs, and ev...
This paper describes a benchmark for stream processing frameworks allowing accurate latency benchmar...
In data streaming we work with large data from multiple sources. We observe overloaded partitions du...
International audienceBig Data applications are increasingly moving from batch-oriented execution mo...