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Adam Glustein - Enabling real-time insights through stream processing in Python | PyData London 2024
Enabling real-time insights through stream processing in Python using Apache Beam, Apache Flink, and CSP, with applications in finance, transportation, and healthcare, and the power to analyze and visualize complex systems.
- Stream processing is about reacting to events in real-time, coordinating input data, and transforming it.
- Apache Beam and Apache Flink are two popular open-source stream processing frameworks.
- CSP (Composable Stream Processing) is a newly open-source library for stream processing in Python.
- CSP provides a modular structure for building stream processing applications.
- Stream processors can be used in various industries, such as finance, transportation, and healthcare.
- Real-time data feeds can be used to gain insights into complex systems.
- Stream processing can be used to analyze and visualize large amounts of data.
- CSP provides a simple and flexible way to build stream processing applications.
- Nodes in CSP can be used to perform various transformations on data.
- CSP provides a way to schedule downstream consumers and execute pipelines in a nice fashion.
- Real-time data can be used to analyze and visualize complex systems.
- CSP provides a way to reason about complex systems and gain insights into their behavior.
- Stream processing can be used to build interactive dashboards for real-time data.
- CSP provides a way to integrate with other libraries and frameworks, such as Pandas and Matplotlib.
- Real-time data can be used to analyze and visualize complex systems.
- CSP provides a way to schedule downstream consumers and execute pipelines in a nice fashion.
- Stream processing can be used to build interactive dashboards for real-time data.
- CSP provides a way to integrate with other libraries and frameworks, such as Pandas and Matplotlib.