IoT and Stream Analytics
-by CNE Systems
In an Internet of Things (IoT) environment, machines, sensors, and devices are connected to networks and data systems. These things or smart objects generate large volumes of fast moving data with huge potential for insight generation.
This generation requires an immediate decision. Every decision has a window of time when information is valuable. Thus assigning deadlines to these decisions and if information arrives too late, the opportunity to make the call based on analytical insights is lost.
This is where we introduce/incorporate Streaming Analytics. Stream Analytics makes it easy to set up real-time analytic computations on data streaming from devices, sensors, web sites, social media, applications, infrastructure systems, and more.
- Streaming Analytics is the ability to constantly calculate statistical analytics while moving within the stream of data. Also Streaming Analytics allows management, monitoring, and real-time analytics of live streaming data.
- Real-time data feeds enable users to analyze constantly churning information as it changes. Viewing events as they unfold accelerates action.
- Decision makers can detect both threats and opportunities in fast moving data and act immediately.
- Real-time integration lets analysts immediately compare new data to historical data to put current conditions in context.
- Nobody has to wait for information to be compiled and decisions are not delayed by drawn out data processes.
- Streaming Analytics involves knowing and acting upon events happening in your business at any given moment.
- Streaming Analytics can help companies identify new business opportunities and revenue streams which results in an increase in profits, new customers, and improved customer service. A Streaming Analytics platform can process millions and tens of millions of events per second.
- Since Streaming Analytics occurs immediately, companies must act on the analytics data quickly within a small window of opportunity before the data loses its value. We will focus on the data that originates from the Internet of Things (IoT)
- Scenarios of real-time streaming analytics can be found across all industries: personalized, real-time stock-trading analysis and alerts offered by financial services companies; real-time fraud detection; data and identity protection services; reliable ingestion and analysis of data generated by sensors and actuators embedded in physical objects (Internet of Things, or IoT); web click stream analytics; and customer relationship management (CRM) applications issuing alerts when customer experience within a time frame is degraded.
Key capabilities and benefits
- Ease of use:Stream Analytics supports a simple, declarative query model for describing transformations. In order to optimize for ease of use, Stream Analytics uses a T-SQL variant, and removes the need for customers to deal with the technical complexities of stream processing systems. Implementing series queries, including temporal-based joins, windowed aggregates, temporal filters, and other common operations such as joins, aggregates, projections, and filters.
- Scalability: Users are able to partition the computation into a number of logical steps within the query definition, each with the ability to be further partitioned to increase scalability.
- Reliability, repeatability and quick recovery:A managed service in the cloud, Stream Analytics helps prevent data loss and provides business continuity in the event of failures through built-in recovery capabilities. This enables customers to go back in time and investigate computations when doing root-cause analysis, what-if analysis, etc.
- Low cost:As a cloud service, Stream Analytics is optimized to provide users a very low cost to get going and maintain real-time analytics solutions.
- Reference data:Stream Analytics provides users the ability to specify and use reference data. This could be historical data or simply non-streaming data that changes less frequently over time.
- User Defined Functions:Stream Analytics has integration with Azure Machine Learning to define function calls in the Machine Learning service as part of a Stream Analytics query.
- Part I: Since the Streaming capabilities adopted in the Stream Analysis handle high data throughput rates, it enables decision makers to form insights from the Internet of Things in time to make a difference.
- Part II: Real-time data engagement lets users ingest information as it arrives from a multitude of sources in an Internet of Things environment. Organizations that invest in high speed data architecture and infrastructure will see returns in the form of happier users analyzing data as it’s generated.
- The Internet of Things creates a constant stream of potential insight waiting for the right minds in possession of the right technology. Streaming data integration and analytics offer a chance for enlightened organizations to use superior analytics to gain a competitive advantage.
Advantages of Streaming Analytics
- Provides Deeper Insight through Data Visualization: Streaming Analytics accelerates decision-making and provides access to business metrics and reporting.
- Offers Insight into Customer Behavior: Streaming Analytics allows companies to gain visibility into what customers are buying, not buying, customer preferences, and dislikes. It allows companies to rapidly respond to customer needs and increase revenues through up-selling and cross-selling of goods and services.
- Remain Competitive: Businesses can identify trends and benchmarks, develop white papers, use cases, and generate forecasts of their company and industry. This helps companies become innovative, remain competitive, and strengthen their brand.