Overview
Stream processing (e.g., Amazon Kinesis, Google Cloud Dataflow, Apache Flink) allows organizations to act on data immediately rather than waiting for batch processing. It is used for data that is generated continuously by thousands of sources.
Key Concepts
- Windowing: Grouping data into time-based chunks (e.g., every 5 minutes) for analysis.
- State Management: Keeping track of information across multiple events in a stream.
- Low Latency: Processing events in milliseconds or seconds.
Use Cases
- Real-time fraud detection.
- Monitoring IoT sensor data.
- Social media sentiment analysis.
- Real-time log analysis and alerting.