Moving Toward Real-time Analytics
In the years since the rise of big data and analytics (Apache™ Hadoop® and associated distributed data systems), we’ve seen a tremendous amount of innovation from teams looking to overcome either use-case specific, industry-specific, or even infrastructure-specific data pipeline challenges. At some point enough people begin innovating and creating similar designs, sharing their solutions at conferences or in online forums, and best practices form around implementing the same design patterns over and over.
One such challenge that folks have been designing around is the need to take in real-time streams of data and provide both real-time dashboards and analytics. These capabilities enable quick decisions as well as the ability to include important historical data to mine for insights and patterns. The Industrial Internet of Things (IIoT) characterized by voluminous data from sensors streaming at high velocities, has been one of the primary drivers for solutions where you need rapid analytics to react and to respond to sudden changes, but also need to include the historical data to perform data modeling and even reconcile billing.