From past few years, a revolution in the field of connectivity has been booming around us, especially in the Internet of Things. Data management has been in practice for two decades now. It had remained relatively dormant ever since its inception until the dawn of the Internet of Things (IoT). The face of the industry is now vigorously changing with almost every business getting transpired by the IoT.
While in the 90’s, the Internet was able to connect around 1 billion users through those precarious dial-up networks, with the onset of mobile phones in the 2000s, around 2 billion users got connected with several other advantages.
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At present, the wave of the Internet of Things has managed to connect whopping 28 billion devices, which is 10 times higher than earlier connectivity phases. By 2020, one can access the Internet from anywhere and can transform any medium right from cars to bracelets into connective devices. With 2.5 billion smartphone users presently, and predicting 5 billion users by 2019, we’re looking towards an outpour of new data in the next few years.
This vigorous growth of IoT is changing data management significantly. As industry experts attempt to curb in big data management, they’re using a blend of workflow strategies, AI and IoT platforms and different hardware configurations.
Assembling & Integrating Big Data
Big data isn’t that easy to be processed and organized, it is, in fact, a strenuous task that to be carried out. Unlike uniform data sets that are easily explained and infused into spreadsheets, big data drag information from unlimited sources. The big data use quite complex methods of data collection including automated reports, sensors, and trend analysis to name a few. And with such a vast source of information streaming in from different avenues, the big data professionals are forced to invest in more time.
Taking into consideration this enormous amount of data storage, organizations have begun to move towards the Internet of Things-enabled Platform-as-a-Service model and avail a cloud-based solution from IoT experts such as Ampcus, as opposed to managing their age-old data storage framework. Unlike, in-house workflow systems which demand updation at regular intervals because of the overloading of the data, Ampcus provides an end-to-end big data solution that includes scalability, compliance, and a tailor-made framework rendering space for all valuable IoT data.
Choosing the right infrastructure for IoT data
Today various data storage architectures are available to manage mammoth-sized data. While some enterprises use New Technology File System, there are still some companies who follow the traditional methods such as the File Allocation Table architecture. Even after using the traditional and modern technologies alike, the enterprises have failed to maintain their own compound servers and hardware at times. This is the situation where the Internet of Things come into play. They are efficient enough in capturing and manage those servers along-with the mammoth-sized data in real-time. They also create intuitive data models and automate decision-making jobs.
They are efficient enough in capturing and manage those servers along-with the mammoth-sized data in real-time. They also create intuitive data models and automate decision-making jobs. These advanced Internet-of-Things-enabled file frameworks and OS witness a paradigm shift in the way techies approach the field of data management.
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