Overview
According to Forrester Research, adoption interest for streaming data analytics has grown 62% during recent years. Organizations are increasingly recognizing the value of real-time data insights.
The Challenge of Real-Time Data
Today, sensors, IoT devices, mobile devices, clickstreams, and transaction data are flooding our logs and streaming in at unprecedented velocities. Applications monitoring temperature sensors or pressure sensors need immediate action on perishable insights.
Real-Time Insights vs. Historical Analysis
To leverage perishable insights, you must build a data model and architecture that supports real-time analytics alongside traditional batch processing. There's a critical distinction between two types of insights:
- Historical insights: Mining historical data helps you make better long-term business decisions
- Short-term insights: Real-time streaming analytics enables immediate tactical responses to emerging patterns
Real-time insights are time-sensitive and lose value quickly if not acted upon immediately. Streaming data analytics platforms capture and process these insights as events occur, enabling organizations to make faster business decisions.
Key Applications
Streaming analytics is particularly valuable for:
- Real-time monitoring and alerting
- Fraud detection in financial transactions
- Anomaly detection in operational data
- Customer behavior analysis
- IoT sensor data processing
Conclusion
The momentum behind streaming data analytics reflects growing business demands for faster decision-making and the availability of technology platforms like Apache Spark Streaming and Apache Kafka that make real-time processing feasible at scale.