Streaming analytics is the analysis of large in-motion data called event streams. These streams include those events that occur as the result of an action or set of actions like a financial transaction, equipment failure, or some other trigger. These triggers can happen within a system at a point in time, for example, a click, a sensor reading, a tweet or some other measurable activity. It enables to perform real time analytics for the internet of things solutions. The more data your business generates, the greater your potential benefits will be from streaming analytics.
Stream analytics helps to get real-time insights from devices, and sensors. Then it deploys the solutions in the applications in the cloud. It streams millions of events per second, provide mission critical reliability and performance, also deliver real time dashboard alerts over data from devices and applications, correlate across multiple streams of data and use SQL based language for development.
Traditional analytics works solely on batch processing. Here the data is processed as per schedule like hourly, daily or weekly. This means that the business runs on the happening of past events or conditions. Therefore, traditional analytics face difficulties in efficiently managing and tracking the consumption of event streams.
Event stream processing can capture events, assess them, make decisions and share the outputs within a specific time windows. It enables you to respond to changing conditions immediately, to improve operations, and to enhance customer interactions.
Applications:
Stream analytics find some interesting applications in the industry like real time stock trading analysis, financial services alerts, real time fraud detection, and data & identity protection services. The analysis of data generated by sensors and actuators, CRM alerts, web click alerts, supply chain alerts and transportation alerts also provide good applications.
Do you need this type of technology?
It depends on the data generation from the applications and the devices you are using in your organization. To know it, rank them as per their importance. Data streams that are critical to your organization’s business will be given priority. The real-time analysis will automatically provide the answer. Many enterprises find that real-time analysis on business-critical streams enhances their ability to respond to customers, identify market conditions, minimize safety risk and enhance security.
Open Source streaming analytics:
Open source analytics is another platform that can provide solutions but it requires skilled persons for coding and customize predictive models for the environment. It requires a visual model building interface, which can slow down the time to value, if not available. They all vary in their interpretation of analytics in streams. These limitations may take a long time for your organization to realize the value. Therefore, it is better to make sure you consider the time to value.
Conclusion:
It is best to consider all the pros and cons before opting for the technology. Stream Analytics is designed to be easy to use, flexible, scalable to any job size and economical. Its benefits can help your business remain competitive with increased efficiency. It enables better business decisions by focusing on live, and streaming data.