Streaming Analytics performs actions on real-time data through the use of continuous queries. It connects to external data sources, enabling applications to integrate certain data into the application flow, or to update an external database with processed information. It enables so-called “real-time business intelligence” (RTBI) is the process of using real-time analytics to deliver information on business operations as they occur. At HorizonX, our team is experienced in delivering high quality, scalable and resilient streaming analytics platforms allowing customers to achieve real- time events, decisions, modelling and reporting.
What is Streaming Analytics?
Streaming analytics (also called real-time analytics) is comparatively newer than historical data analysis techniques. Stream processing analyses and performs actions on real-time data through the use of continuous queries. Streaming analytics connects to external data sources, enabling applications to integrate certain data into the application flow, or to update an external database with processed information.
The ability to extract information from operational data in real time is critical for a modern, agile enterprise. The faster you can harness insights from data, the greater your advantage in driving revenue, reducing costs, and increasing efficiency.
Why do Streaming Analytics?
Historical data tells us what happened in the past while real-time analytics tells you what is happening right now. This essential difference explains the advantages of looking at real-time data:
- Data Visualization. A set of historical data can be placed into a single chart to communicate an overall point. But streaming data can be visualized in a way that updates in real time to show what is occurring at every single moment
- Business Insights. Whenever an important business event occurs, it will first appear in the relevant dashboard. If the hourly sales at one of the aforementioned grocery stories plummets at an unusual time, then an alert can be triggered to tell management of a serious problem at that branch location
- Increased competitiveness. Businesses can discern trends and set benchmarks much more quickly, allowing them to use this data to surpass competitors who are still using the slower process of batch analysis.
Real-time streaming analytics (RTSA), has the following value to businesses:
- Cutting preventable losses. Streaming analytics can prevent or at least lessen the damage of incidents such as security breaches, stock exchange meltdowns, airplane crashes, manufacturing defects, customer churn, and social media meltdowns.
- Analysing routine business operations. All of these can be monitored in real time: manufacturing closed-loop control systems; IT systems; field assets such as trucks, oil rigs, vending machines and radio towers; and financial transactions such as authentications and validations.
- Finding missed opportunities. The streaming and analysing of Big Data can help companies to learn from customers as well as immediately recommend, upsell, and cross-sell to them based on what the information presents.
- Create new opportunities. The existence of streaming data technology has led to the invention of new business models, product innovations, and revenue streams. Tractors could be implemented with soil sensors. Clothing manufacturers could add wearable health technology to its products.
Real-time streaming analytics (RTSA) enables so-called “real-time business intelligence” (RTBI) is the process of using real-time analytics to deliver information on business operations as they occur. (“Real-time” refers to near-zero latency. In practical terms, the phrase means that information becomes available anywhere from milliseconds to five seconds after the fact.)
How to do Streaming Analytics?
In RTSA, Data is analysed in motion as it arrives. Every incoming event is process-able. Events can be stored later or in parallel. Immediate actions are possible after processing.In descriptive, predictive, and prescriptive analytics, one exports a set of historical data for batch analysis. In streaming analytics, one analyses and visualizes data in real time. Real-time analytics can be used for purposes such as these:
- To make operational decisions and apply them to business processes, transactions, or other production activities in real time and on an ongoing basis
- To apply pre-existing predictive or prescriptive models
- To report current and historical data concurrently
- To receive alerts based on certain, predefined parameters
- To view real-time displays or dashboards in real time on constantly-changing transactional data sets such as the hourly sales of a set of regional grocery stores