Abstract
References
- Aktas, D. E., & Aktas, M. S. (2020). Real-time pattern detection methodology for monitoring student behaviour on e-learning platform in the field of financial sciences: Case study. 28th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
- Aktas, M., Aydin, G., Donnellan, A., Fox, G., Granat, R., Lyzenga, G., McLeod, D., Pallickara, S., Parker, J., Pierce, M., Rundle, J., & Sayar, A. (2005). Implementing geographical information system grid services to support computational geophysics in a service-oriented environment. NASA Earth-Sun System Technology Conference. University of Maryland, Adelphi, Maryland.
- Apache Flink. (2023). Apache Flink documentation. Retrieved from https://flink.apache.org/
- Apache Kafka. (2023). Apache Kafka documentation. Retrieved from https://kafka.apache.org/
Abstract
In today's very competitive business world, being able to use data to its fullest in real time has become a key differentiation. This paper looks at how two cutting-edge technologies, Apache Flink and Apache Kafka, work together and how they are changing the way real-time data is processed and analyzed. With its fault-tolerant framework made for collecting data from many sources, Apache Kafka is a leader in reliability and scalability when it comes to ingesting data. Apache Flink is the perfect partner for Kafka because it is great at stream processing and low-latency event handling. This paper carefully explains how these technologies work together to create a complete set of tools for handling and analyzing data in real time. The paper goes into detail about how Flink and Kafka can work together, showing how data streams can be handled and intelligently put together to produce insights that can be used. This set of tools, which was created after a lot of study and real-world experience, helps organizations that want to start using real-time data in new ways. Evaluations of performance, scalability, and real-world applications show that this integrated method has a real effect. Beyond just talking about ideas, this study paper gives organizations a step-by-step plan for how to use real-time data to improve their decision-making. By taking advantage of how well Flink and Kafka work together, companies can become more flexible, quick to respond, and creative.
Keywords
: Complex event processing Event management systems Real-time data management Streaming data
References
- Aktas, D. E., & Aktas, M. S. (2020). Real-time pattern detection methodology for monitoring student behaviour on e-learning platform in the field of financial sciences: Case study. 28th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
- Aktas, M., Aydin, G., Donnellan, A., Fox, G., Granat, R., Lyzenga, G., McLeod, D., Pallickara, S., Parker, J., Pierce, M., Rundle, J., & Sayar, A. (2005). Implementing geographical information system grid services to support computational geophysics in a service-oriented environment. NASA Earth-Sun System Technology Conference. University of Maryland, Adelphi, Maryland.
- Apache Flink. (2023). Apache Flink documentation. Retrieved from https://flink.apache.org/
- Apache Kafka. (2023). Apache Kafka documentation. Retrieved from https://kafka.apache.org/
Details
| Primary Language | English |
|---|---|
| Subjects | Environmental and Sustainable Processes |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | December 19, 2023 |
| Publication Date | November 30, 2023 |
| Published in Issue | Year 2023Volume: 24 |
