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Tools and frameworks for processing streaming data.
Tools for streaming data processing in Python are designed to handle and analyze continuous streams of data in real-time. These tools are essential in scenarios where immediate insights and actions are required, such as monitoring network traffic, financial transactions, social media feeds, or sensor data in IoT applications. They enable data engineers and scientists to process, aggregate, filter, and analyze data as it arrives, providing the capability to make decisions swiftly based on the latest information.
Distributed Event Streaming Platform
Distributed event streaming platform capable of handling trillions of events a day. Used for building real-time streaming data pipelines and applications with high-throughput, fault-tolerance, and scalability.
Stream Processing Framework
Framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Known for high performance in streaming data processing with exactly-once semantics.
Real-Time Computation System
Real-time computation system making it easy to process unbounded streams of data reliably. Fast and scalable distributed real-time computation framework for stream processing.
Scalable Stream Processing
Extension of Apache Spark API enabling scalable, high-throughput, fault-tolerant processing of live data streams. Integrated within Spark ecosystem for complex real-time data processing tasks.