Streaming Analytics Explained

In the Big Data era, businesses desire to incorporate any and all kinds of data in their decision-making processes. Instead of second-guessing important business decisions, businesses tend to observe and measure, then decide accordingly. This is a good approach – but it increases time and effort. Thus, it is important to understand why and how the data driven culture of our generation creates the desire of real-time, streaming data. Streaming analytics work by allowing organizations to set up real-time analytics computations on data being streamed from devices, applications, marketing data sensors, websites, etc.; and provides instant and reasonable time-sensitive processing with language integration for intuitive specifications. It uses a simple SQL version and reduce the complexity of the streaming processing system. Streaming analytics is also called by several names: Real-time Analytics, Complex Event Processing, Real-Time Streaming Analytics, and Event Processing. It’s popularized by Apache Storm which details a system for processing streaming data in real time, implicating many use cases such as: real-time analytics, machine learning, continuous computation, distributed RPC, and more.

Streaming Analytics Infrastructure

Importance of Streaming Analytics

Streaming analytics allows companies to analyze data as soon as it becomes available, which is important before data alteration gets in the way of accurate analysis. This helps to identify new business opportunities, resulting in increases to profits, new customers, and improved customer service. Streaming Analytics platforms process millions of events per second. It also helps in providing security by enabling companies to rapidly connect to various events that detect threats and risks. Ultimately, streaming analytics plays a key role in a data-driven organization. The following are some use cases:

• Traffic Monitoring – TFL London Transport Management System
• Predictive Maintenance
• Stock Market Surveillance
• Smart Patient Care
• Supply chain optimizations
• Surveillance and Fraud Detection – Uber
• Sport analytics – Overlaying realtime analytics on Football Broadcasts
• Computer system and network monitoring

Why is Streaming analytics needed?

Big data has established new insights from processing massive amounts of raw data. However, some insights aren’t made equal. Some insights are useful in niche situations and determining that can dictate the success of a company. Regardless, the main strength of streaming analytics remains that it can provide insights faster, often within seconds for milliseconds. If you enjoyed this post you might also find Challenges and Uses of Real-time Analytics.