Big Data is the frontier of an organization’s ability to manage all sorts of data it needs to process and serve their target customers.
An enormous amount of sensing devices are becoming popular day by day as they have enabled big or fast/real-time data streams. Stream analytics uses a technique called deep learning to facilitate analytics and learning in the IoT domain.
As big data is proliferating rapidly, a huge stream of data is being efficiently managed and processed at a higher rate. The real-time and historical data can be easily blended to support predictive analytics and rich visualization.
Data-in-motion is as valuable as data-at-rest
Real-time insights can be now generated from this enormously flowing data to give organizations valuable information that they can act on before they start losing their value. Streaming analytics captures data from devices with low latency to identify business patterns that have happened or are about to happen. This again falls under predictive analysis. Due to the growing volume of data across various platforms, promising to enhance its quality and management is not a straightforward task. For these complex requirements, traditional inference and learning approaches have found it mandatory to go beyond their norms and revolutionize their machine learning techniques. This has in-turn opened new doors for machine learning (ML) with multi-modal data streaming analytics.