Using a stream processing platform
built for event-driven architectures
Event-driven processing is a programming paradigm focusing on responding to events or messages in real time. It is commonly used in systems that require real-time processing. The main advantage of this approach is that it allows the system to be more responsive and efficient since it can react immediately to events as they occur. Dealing with problem events faster than other companies or providing suggestions for preventative actions based on events as they happen provides a definite competitive edge.
Real-time and streaming analytics
Streaming analytics use cases include:
- Real-time stock trading
- Fraud detection
- Dynamically updating location information
RisingWave allows processing data while it is in motion to extract needed business value promptly and efficiently. It can gather data from various applications, sensors and devices, social media apps, websites, and more.
Processing the data in a continuous stream ensures that queries always return the most up-to-date information available.
Continuous monitoring and alerting
Monitoring and alerting use cases include:
- System monitoring to respond to failure concerns
- Security reporting to deal with potential threats
- Automating remedial actions based on possible errors
RisingWave helps to continuously monitor systems or applications to detect compliance and risk issues. Any anomalies can be detected quickly, so the response is swift and accurate. Together, monitoring and alerting provide a proactive approach to system management, allowing teams to identify and resolve issues before they escalate into full-blown crises.
Streaming ETL and data pipeline
Streaming and data pipeline use cases include:
- Internet of Things
- Fraud detection
- Real-time payment processing
RisingWave can easily build streaming data pipelines wherever the data originates and regardless of its intended destination. Some data sources include messaging queues, OLTP databases, or microservices, and data targets can be data warehouses or data lakes. The data will be in sync in seconds since the incoming data is processed incrementally rather than in batches.