Internet of Things (IoT) technology started a few years back, it is now on a roll and it will be inevitable in our lives very soon. Many companies and governments are already enfolding IoT now – either for better services or, well, for income- but when you look at the predictions, can you blame them? Ericcson estimates that there will be a total of approx. 28 billion connected devices worldwide by 2021- which is 4 times more than Gartner forecasted for 2016. McKinsey also predicts that in 2025, the economic impact of IoT could reach $3.9 trillion to $11.9 trillion a year.
However this ‘cool’ data ecosystem with its high-tech heterogeneous sensors are nonentity without being analyzed real-time by Stream Processing. Stream processing is a technology enabling its applications to act-on (collect, integrate, visualize, and analyze) real-time streaming data, while the data is being produced. In simpler words, Stream Processing gives us the ability to quickly process large amounts of data from multiple sources, in real-time.
Stream processing and streaming analytics process big data, cloud, and internet of things without disrupting the activity of existing sources, storage, and enterprise systems. The most obvious benefit of stream processing is its ability to treat data not as static tables but as an infinite stream that goes from what happened in the past into what will happen in the future. In addition to that, it is flexible to adapt to changing analytic and business needs and processes simultaneously to complex event streams faster. It also involves running data as it arrives through a query, so the outcomes are generated as a continuous operation. For example, a company can analyze their sales in real-time while building core applications that re-order products, and adjust prices by region, in response to incoming sales data.
A good Real-Time Stream Processing solution solves different challenges in IoT data such as;
- Processing huge amounts of streaming events (collect, regulate, filter, automate, predict, monitor, alert)
- Fast integration with infrastructure and data sources
- Detecting anomalies faster
- Real-time responsiveness and deployment to fluctuant market conditions and requirements
- Performance and scalability as data volumes increase in size and complexity
- Analytics at IoT scale: Live data discovery and monitoring, continuous query processing
- Automated alerts
- Data security (via real-time alerts)
- Instant reaction to customer insights
IoT Real-Time Stream Processing Uses
Today, Stream Processing is used in every industry and government bodies where stream data is generated through ioT data.
- For instance we are no strangers to Smart Cities where smart traffic lights, using traffic volume data, bus stops with digital arrival time boards, smart meter reading systems, smart intersection systems and intelligent street lighting. You may think managing and analyzing these data sets should not be very complicated. However when you integrate these real-time data with each other, add video/weather data, connect information from last week with cross-referencing with real-time demographics etc., this becomes a complex Big Data challenge only real-time stream process can solve.
- The same logic goes with Home Automation. At an individual home level, again the collection, integration, visualization, and real-time analysis of the data might not be complex however once millions of home appliances connect to the same service while using the consumer behavior data and take real-time actions (e.g. fridge ordering yoghurt) the scaling challenge for the data processing is only solved with real-time stream processing.
- Data Security is also a major problem for IoT. In fact it is expected that data breaches will increase in 2017. Today’s alert systems require usage and pattern analysis for all data, across all systems, in real-time and only stream processing is able to filter and aggregate and analyze continuous collection of data in milliseconds, so nothing gets overseen or outdated.
Some other use cases where stream processing can solve business problems in IoT include network monitoring, fraud detection, smart order routing, pricing and analytics, market data management and algorithmic trading.
More and more applications and services are going on-line in the ecosystems. As organizations and governments gather more real-time data from users, systems, and smart devices; Stream Processing enables them to handle the challenges of real-time data and gain richer analysis with more scalable applications. This ability provides organizations to improve customer satisfaction and efficiency in their operations.