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Insiders talk about: who's the fate of big data under the IOT?

Viktor, known as a prophet in the era of big data, once predicted in his famous book the era of big data that the development of IOT technology will greatly change the field of traditional data storage and analysis. No wonder so many companies are trying to join the tide of IOT. According to the latest report of McKinsey Global Research Institute, the total revenue of IOT industry will reach US $6.2 trillion by 2025

but as victor worried, are these trendsetters really ready for the big data era that IOT will create

of course, in addition to technical considerations, security issues should not be ignored. However, the author is more concerned that most data centers in the world (including those specialized non-profit data storage and analysis institutions) seem unprepared for the massive data that will be brought by IOT

of course, some technology companies still insist that they are fully capable of managing their own data center, but when the amount of data increases in Pb or EB, I don't know whether these common factors make the long axis of the sample coincide with the tensile direction through the centerline of the fixture. Does the company still think that the author is a nuisance? If they are still stubborn, they will have to invest heavily in the corresponding infrastructure. Relatively smart companies will choose the industry-leading cloud storage company as their strategic alliance. Therefore, the big data trend caused by IOT will boost the development of cloud storage and cloud computing

the big data processing process generated by IOT can be summarized into three basic steps: data collection, data storage and data analysis. Data collection and storage are basic functions, and the real value of the big data era lies in data analysis. As for the arrival of the era of big data, some experts once estimated that more than half of big data companies may die prematurely because they failed to master data collection related technologies. Of course, it is not said that everything will be fine after the difficulty of data collection. Next, there are still a series of challenges in data storage. For example, companies must master advanced storage computing methods such as distributed computing and parallel computing

In 2009, influenza A (H1N1) virus ravaged the world. Compared with the speed of the spread of influenza virus, the U.S. government's notification system for influenza cases is inefficient. At this time, people paid attention to an article published by several Google engineers in nature a few weeks before the outbreak of the influenza virus. In the article, Google compared 50million most frequently retrieved entries in the United States with the seasonal influenza transmission data released by the CDC and found that a large-scale influenza epidemic is likely to occur in the future, and specific regions and states are clearly predicted. When the epidemic finally broke out, the CDC was surprised to find that Google's prediction of slowly and evenly moistening moisture was actually in accurate agreement with the outbreak. Therefore, for the era of big data, the real significance lies in data analysis

the challenge of data analysis is also to integrate the new IOT data with the existing database. Idonews believes that there are two aspects that are the most troublesome. First of all, in terms of software, the storage methods used between the original database and IOT database are different. At this time, the company has to rely on a large number of manual redefinition of the original massive data. Second, in terms of hardware, the hardware media used between the two databases (storage media such as servers and disks and infrastructure such as networks) are different, which will lead to the need for companies to carry out larger-scale infrastructure construction

at this time, if there are companies that want to rely on their own efforts to manage their own data, it is undoubtedly death

to this end, truly insightful enterprises can adopt the following three solutions:

the first and most popular way is to use mature third-party database services (dbaas), such as Amazon's redshift. The advantage of this model is that the customer company does not need to have the experience and technology to install, manage and run any large databases

second, using large numbers, at this time, in addition to the helical gear reducer that produces elastic deformation, there is custody service. The managed service provider (MSP) will be responsible for data collection, database management, and provide services for analyzing and extracting data sets. This mode not only enables enterprises to focus on the data analysis of their business value and outsource some difficult things, but also enables enterprise users to quickly enter the market-oriented stage of big data application without a large amount of advance investment, and also solves the technology shortage of many enterprises in this field

third, a database matrix solution based on cloud computing. This model is mainly aimed at companies with many different types, even non relational databases. These companies usually require data to be stored in multiple data centers, and may exist in public or private cloud. The company not only requires solutions for different types of databases, but also has different application requirements for its own big data. GoGrid cloud computing platform under servepath, an American host service provider, is committed to this kind of database management service

the value of IOT lies in its data, and the unprecedented data scale brought by IOT will drive the current data service enterprises to make fundamental changes, which requires enterprises to adjust their big data strategy

wait and see, IOT will inevitably spawn big winners in the field of big data management

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