What is Real Time Analytics?
Real-time analytics makes it possible for organizations to capture live streams of data, process them very quickly, and extract insights or perform operations on the data in real time or near-real-time. It is based on stream processing technology that can handle a very high throughput of event data.
There are two types of real time analytics:
- On-demand analytics—providing data or computation results to users or applications in real time. For example, displaying the current product price to a user on an eCommerce site.
- Continuous analytics—processing events on a continuous basis and streaming the results to end users, applications, or a data store. For example, showing live stock market data on a dashboard in a financial institution.
Real-time analytics has many uses in the digital economy. For example, it can help businesses track customer data and respond with personalized offers, improving customer engagement. It can enable rapid, automated response to shifts in the market, enabling dynamic pricing on eCommerce sites. Another common use is to process massive volumes of log or sensor data, from IT systems or internet of things (IoT) devices, and using them to drive business decisions.
In this article, you will learn:
- What are Stream Processing Frameworks?
- Top Stream Processing Frameworks
- Amazon Kinesis
- Azure Stream Analytics
- Apache Spark
- Best Practices for Real Time Analytics
