|By Tom Leyden||
|March 9, 2014 12:15 PM EDT||
Previously in this series, I explained the evolution of unstructured data and how storage requirements have changed over the past decade due to changes from above and below: the massive growth in unstructured data, mostly immutable data, requires cost-efficient, easy-to-scale storage architectures without the complexity of file systems. I noted that object storage was designed for this purpose, and that in addition to scalability and efficiency benefits, object storage also provides great benefits when it comes to accessibility as REST and other protocols make it very easy for applications to connect to the storage directly and give users access to their data through all sorts of mobile and web applications.
I also explained how information-sensing devices are not exclusive to scientific analytics environments: think of cameras and smart phones but also cheap network cameras for home security, thermostats that warm up our houses when we are on our way home, smart fridges or watering devices that allow us to keep our plants healthy and happy, even when we are on a vacation. This wave of innovation based on the capabilities to generate, process and leverage data in apps and devices is now popularly called The Internet of Things (IoT).
IoT is not a new concept. Wikipedia says: “The term Internet of Things was proposed by Kevin Ashton in 1999 though the concept has been discussed in the literature since at least 1991.” In its early stages, the concept relates to the use of radio-frequency identification (RFID) and how “if all objects and people in daily life were equipped with identifiers, they could be managed and inventoried by computers.” Finally, Wikipedia adds that, “Equipping all objects in the world with minuscule identifying devices or machine-readable identifiers could transform daily life.” And this is exactly what is happening today and also what makes IoT more important than ever.
Apple retail stores are full of “gadgets” that can make our lives easier, healthier and more enjoyable. Google has jumped on the gadget bandwagon with Google Glasses. And in terms of sizing this gadget market, Gartner is predicting that there will be over 25 billion IoT devices by 2020.
So what does this have to do with the topic at hand – Object Storage?
The one thing all those IoT devices have in common is that they log, generate and process data and turn it into information that can help us to keep track of our workouts, optimize energy consumption or bring our household automation (which sounds so much better in Italian: “domotica”) to the next level.
The fact that all these IoT devices are connected to the Internet means that more and more data will be uploaded from those devices. A lot of it is very small data, but from a volume point of view, if we take the sum of all the data those devices are generating, we are talking about exabytes and exabytes of information in the form of unstructured data – almost too much to fathom!
Traditional storage is simply not capable of handling these types of data. So, Object storage has emerged as the logical paradigm for storing such data. Designed for large volumes of data, it supports distributed environments and the applications that run on these devices can integrated directly with the storage layer. Not all object storage platforms qualify for IoT data, however. The reasons for this are:
- Small files in particular are a challenge for many object storage platforms: NoFS (No file System) becomes a key requirement
- Performance needs to scale in all dimensions, especially IOPS and latency.
- Different data types and different applications require different data protection mechanisms, as well as flexible architectures.
IoT has been around for a while but things are only getting started. Innovation doesn’t stand still, so who knows how data storage requirements will evolve over the next decade?
So here ends my three-blog series, which covered the evolution of digital data over the past 4 decades and illustrated how storage platforms have evolved to meet the requirements of new data types and applications. Object storage is designed for long-term storage strategies but we understand it will probably not be the end point.
Companies can harness IoT and predictive analytics to sustain business continuity; predict and manage site performance during emergencies; minimize expensive reactive maintenance; and forecast equipment and maintenance budgets and expenditures. Providing cost-effective, uninterrupted service is challenging, particularly for organizations with geographically dispersed operations.
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