Real-time insights can be challenging ...
A significant factor in competitive advantage in modern businesses is determined by the availability of business insights and information to make decisions in real-time. Real-time streaming analytics is the technology that makes this a reality.
First generation stream processors pose several challenges.
Standard operators such as windows and patterns too need complex code. Most enterprises can’t afford the required time and skills.
Even a basic highly available deployment needs 5+ servers. Such deployments are complex, take time to setup, and are expensive to maintain.
Slow to Change
Lack of visibility into processing flow, and lack of tooling hinder enterprises from quickly adapting to rapid changes.
Apply Lightweight Adaptable Streaming SQL
- Collect events from multiple event sources using various data formats.
- Does preprocessing by deploying at the edge.
- Process stream of events in real-time using Streaming SQL queries.
- Summarize and correlates events in memory and by integrating with data stores.
- Notifies interesting event occurrences via alerts and service calls.
- Visualize the summarizations via dashboard.
Stream Processor Reference Architecture
Collect events through multiple transports and messaging formats. Use Streaming SQL to process streams, detect complex events and do prediction using machine learning models. Generate and notify alerts in real-time and visualize them with real time dashboards.
Process millions of events per second in real-time
Only stream processor that can provide high availability, 100K+ throughput with just two nodes, and scale to 30+ billion events per day with Kafka.
Adapt to the market faster with quicker development times
The state-of-the art IDE for Siddhi Streaming SQL language includes smart editing, event replay and simulation, and debugging capabilities.
Investigate the past, predict the future
Gain insights using past performances, serve pre-trained and online machine learning models and perform realtime predictions with streaming SQL.
Enable managers to manage their business rules and visualize output
Empower business users to create and dynamically deploy business rules through easy to use graphical UI, and let them make better decisions utilizing real-time dashboards.
Build smarter devices with edge analytics
Make devices smarter by deploying Siddhi (<2MB) on the edge and scale IoT analytics to handle massive amount of data.
Enable insights into all your systems
Work out of the box with popular data formats, transport protocols and connect to over 100 legacy and cloud services via connectors and agents.
WSO2 Stream Processor is currently used in over 100s of enterprises worldwide, including many Fortune 500 organizations.
State of Arizona monitors and manages their Private PaaS in real time to improve efficiencies and trim costs.
Capgemini generates real-time insights for United Nations Relief Work Agency from millions of refugee data.