Enabling real-time analytics with big data
Performing analytics by streaming data from applications at large volumes is a common scenario for companies that need to make quick decisions based on events that are happening in real time. The idea behind real-time analytics is that companies can make decisions soon after the data event has been captured. Here are a few examples:
- Detecting patterns on financial transactions: The first blueprint in this chapter discussed using Data Explorer pools and Azure Machine Learning to detect credit card fraud. In financial services, however, there are several other opportunities to employ real-time analytics and machine learning for other scenarios, such as providing financial advice and risk assessments based on current market situations.
- Optimizing retail websites based on user flow: This scenario can be used to read log data from web applications that describe how an e-commerce website is being used in response to promotions and, with...