Data Management in Iot Using Big Data Technologies And Tools
Journal Title: International Journal of Engineering and Science Invention - Year 2018, Vol 7, Issue 1
Abstract
The Internet of Things (IoT) [1] is on its way to becoming the next technological revolution. Given the massive amount of revenue and data that the IoT will generate, its impact will be felt across the entire big data universe, forcing companies to upgrade current tools and processes, and technology to evolve to accommodate this additional data volume. Managing and extracting value from IoT data is the biggest challenge that companies face. Organizations should set up a proper analytics platform/infrastructure to analyze the IoT data. An IoT device generates continuous streams of data in a scalable way, and companies must handle the high volume of stream data and perform actions on that data. The actions can be event correlation, metric calculation, statistics preparation, and analytics. In a normal big data scenario, the data is not always stream data, and the actions are different. Building an analytics solution to manage the scale of IoT[8] data should be done with these differences in mind.From a technology perspective, the most important thing is to receive events from IoT-connected devices. The devices can be connected to the network using Wi-Fi, Bluetooth, or another technology, but must be able to send messages to a broker using some well-defined protocol. Once the data is received, the next consideration is the technology platform to store the IoT data. Many companies use Hadoop[3] and Hive[11] to store big data. But for IoT data, NoSQL document databases like Apache Couch DB[14] are more suitable because they offer high throughput and very low latency.
Authors and Affiliations
B. Sobhan Babu, T. Ramanjaneyulu, I. Lakshmi Narayana, K. Srikanth
Keywords
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