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Stream Analytics: Data Streaming and Real-time Analytics

Stream Analytics is a real-time analytics and event processing designed to analyse a huge pool of fast streaming, high volume data received from multiple sources simultaneously. Data stream is triggered by certain actions such as a new transaction, a social media mention, a click to a display ad, or an equipment failure. These actions trigger workflows such as storing transformed data, creating alerts (e.g. machine failure, problem escalation), and feeding information to a reporting tool. Data may come from Internet of Things (IoT) devices, web interaction, applications, machine sensors, and mobile devices.
Stream Analytics is a real-time analytics and event processing designed to analyse a huge pool of fast streaming, high volume data received from multiple sources simultaneously. Data stream is triggered by certain actions such as a new transaction, a social media mention, a click to a display ad, or an equipment failure. These actions trigger workflows such as storing transformed data, creating alerts (e.g. machine failure, problem escalation), and feeding information to a reporting tool. Data may come from Internet of Things (IoT) devices, web interaction, applications, machine sensors, and mobile devices.

Real-time stream analytics has been deployed and has helped a large range of industries such as:

Finance: risk management, stock market monitoring, transaction processing, payment fraud detection,

Transportation/Fleet Operations/Logistics: traffic management, route planning, GPS cargo and delivery tracking, driving behavior monitoring, fuel management;

Healthcare: biometric monitoring, real-time monitoring of health-conditions, clinical risk-assessment, client-state analysis, ICU monitoring and alerts;

Retail/Customer Service: real-time omni-channel promotion, website fraud detection, demand management and inventory replenishment, customer behavior analysis and operations improvement;

Manufacturing/supply chain: real-time monitoring, predictive maintenance, disruption/risk assessment, post-manufacturing support and service;

Home Security: data stream analysis from IoT, smart protection, and alert systems;

IT: fraud detection, alert, and system maintenance

Stream Analytics Architecture

azure stream analytics architecture
Stream Analytics Architecture in Azure

Advantages of Stream Analytics

Data Visualisation

Streaming data monitored real time allows companies to manage Key Performance Indicators (KPIs) on a daily basis. Providing opportunity to keep a good eye on most pressing and important metrics for the operation and do immediate intervention and adjustment as necessary.

Increased Competitiveness

In highly competitive and cutthroat business, streaming analytics provide competitive advantage by identifying trends earlier and benchmarking faster. Doing it first is the only way to get the competitive edge over those who are lagging.

Cutting Preventable Losses

Prevent and reduce losses from manufacturing issues, social media crisis, customer chur and critical security breaches. With real-time analytics, save crucial time to prevent these issues or lessen the negative impact with timely intervention.

Business Insights

Identify out of the ordinary events which merit investigation. This can be abnormal behavior that needs to be flagged, fraud detection, incoming trend and other unlikely occurrences that will show up on the dashboard for immediate attention.

Find Missed Opportunities and Create New Ones

Streaming data can uncover hidden patterns, identify correlations and provide other insights. Businesses can use this information to cross-sell and up-sell clients. Insights from streamed data can identify predictability that will allow an organisation to cut cost, grow sales and resolve recurring problems. It can also result to finding opportunity of creating product innovation, new revenue stream and even developing a new business model.

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