Stream Processing + IoT + Enterprise Data Lake

The value of the Internet of Things (IoT) comes from the volume of data it will produce.  Gartner, predicts that the Internet of things and personal computing will unearth more than $1.9 trillion in revenue before 2020; Cisco thinks there will be upwards of 50 billion connected devices by the same date; IDC estimates technology and services revenue will grow worldwide to $7.3 trillion by 2017 (up from $4.8 trillion in 2012).

With the explosion of the IoT and the mass amounts of data that will be collected, getting the data processed will be key – which makes stream processing essential.  Stream processing is a technology that allows for the collection, integration, analysis, visualization, and system integration of data, all in real time, as the data is being produced, and without disrupting the activity of existing sources, storage, and enterprise systems.  The IoT data flows in at a constant stream and it’s data needs to be analyzed in real time as it streams into the database.  This process takes place when the data is being pushed from the device to the data lake and happens in what can be referred to as the memory grid.

Stream processing is being used by early IoT and Enterprise Data Lake adopters, such as zData Inc.,  to analyze new types of sensor and machine data in real time. Stream processing will in real time – analyze sensor and device data,  stream data through into existing data warehouse and Hadoop platforms, provide continuous process automation from operational systems, and merge real-time data with historical stored data for comparisons.  This processing will be an essential component for Big Data – as data needs with the Internet of Things grow at a rapid rate.