With the rapid development of the Internet, especially in recent years, with the widespread use of social networks, Internet of Things, cloud computing and a variety of sensors, unstructured data featuring a large number, a large variety, and strong timeliness is constantly emerging. The importance of the data storage and analysis technology is difficult to process a large amount of unstructured information in real time, and the concept of big data came into being. How to acquire, aggregate, and analyze big data has become a hot issue of widespread concern. Introduce the concept and characteristics of big data, discuss the typical characteristics of big data, analyze the core issues such as correlation analysis and real-time processing to solve big data, and finally discuss the various challenges that big data may face. "Big data" is a hot word in the IT industry in recent years. The application of big data in various industries has gradually become wider. For example, in the two sessions in 2014, we listened the most to big data analysis. So, what is big data? Is the big data concept, how to understand the big data concept, let's take a look. Big data, or huge amount of data, means that the amount of data involved is so large that it cannot be captured, managed, processed, and organized in a reasonable time through the current mainstream software tools. More positive purpose information. (In the Big Data Era written by Victor Meyer-Schonberg and Kenneth Cookee [2] Big data refers to shortcuts that do not use stochastic analysis (sample surveys), but use all data methods. The 4V features of big data: Volume, Velocity, Variety, and Value. The earliest reference to the term "big data" can be traced back to Apache's open source project Nutch. At the time, big data was used to describe a large data set that needed to be batch processed or analyzed simultaneously to update the web search index. With the release of Google MapReduce and Google File System (GFS), big data is no longer just used to describe a large amount of data, but also covers the speed of processing data. As early as 1980, the famous futurist Alvin Toffler enthusiastically praised big data as "the third wave" in the book "The Third Wave". The tidal movement of the tide. However, since about 2009, “163 Big Data†has become a popular vocabulary in the Internet information technology industry. The US Internet Data Center points out that data on the Internet will grow by 50% every year, every two years. It will double, and more than 90% of the world's data is produced in recent years. In addition, the data does not simply refer to the information published by people on the Internet. There are countless industrial equipment, automobiles and meters on the world. Digital sensors measure and transmit changes in position, motion, vibration, temperature, humidity and even chemicals in the air, and generate massive amounts of data. Big data is a kind of appearance or feature of the Internet to the present stage. There is no need for it or it can be awe-inspiring. Under the background of technological innovation represented by cloud computing, these are difficult to collect and use. The data is easy to use, and through the continuous innovation of all walks of life, big data will gradually create more value for human beings. Secondly, if you want to systematically understand big data, you must decompose it comprehensively and meticulously. I set out to start from three levels: The first level is theory. Theory is the necessary way of cognition, and it is also the baseline that is widely recognized and disseminated. I will understand the industry's overall depiction and characterization of big data from the definition of big data; from the discussion of the value of big data to deeply analyze the preciousness of big data; understand the development trend of big data; from the big data privacy this is particularly important The perspective examines the long-term game between people and data. The second level is technology, which is the means of the value of big data and the cornerstone of progress. I will explain the whole process of collecting, processing, and storing big data from cloud computing, distributed processing technology, storage technology and sensing technology. The third level is practice, and practice is the ultimate value of big data. I will describe the great picture that big data has already shown and the blueprint that will be realized from the big data of the Internet, the big data of the government, the big data of the enterprise and the big data of the individual. Compared with traditional data warehouse applications, big data analysis has the characteristics of large data volume and complex query analysis. The article "Architectural Big Data: Challenges, Current Situations and Prospects" published in the Journal of Computers lists several important features that big data analysis platforms need to have. For the current mainstream implementation platforms - parallel database, MapReduce and a mixture based on the two The architecture is analyzed and summarized, and their respective advantages and disadvantages are pointed out. At the same time, the research status in various directions and the author's efforts in big data analysis are introduced, and the future research is prospected. The four "V" of big data, or features have four levels: First, the amount of data is huge. From the TB level, to the PB level; second, the data types are numerous. The web logs, videos, pictures, geographic location information, etc. mentioned above. Third, the fast processing speed, 1 second law, can quickly obtain high-value information from various types of data, which is also fundamentally different from traditional data mining technology. Fourth, as long as the data is used reasonably and correctly and accurately analyzed, it will bring a high value return. The industry has classified it into four "V"s - Volume, Variety, Velocity, and Value. To some extent, big data is the cutting-edge technology for data analysis. In short, the ability to quickly obtain valuable information from a wide variety of types of data is big data technology. It's important to understand this, and it is this that has made the technology a potential for many companies. Big data can be divided into areas such as big data technology, big data engineering, big data science, and big data applications. The most talked about is big data technology and big data applications. Engineering and scientific issues have not been taken seriously. Big data engineering refers to the systematic engineering of planning, construction, operation and management of big data; big data science focuses on the law of discovering and verifying big data and its relationship with natural and social activities in the development and operation of big data networks. Internet of Things, cloud computing, mobile Internet, car networking, mobile phones, tablets, PCs, and a wide variety of sensors all over the globe are all sources of data or carriers. Some examples include weblogs, RFID, sensor networks, social networks, social data (because of the data revolutionary society), Internet text and documents; Internet search index; call detail records, astronomy, atmospheric science, genomics, biogeochemistry, biology , and other complex and / or interdisciplinary research, military reconnaissance, medical records; photographic archives video archives; and large-scale e-commerce. For the average enterprise, the role of big data is mainly reflected in two aspects, namely, the analysis and use of data and the secondary development project. By analyzing the big data of sheet metal information, not only can the hidden data be mined, but also through these hidden information, through the sales of the entity, the source of the customer can be enhanced. As for the secondary development of data, it is used in network service projects. By summarizing and analyzing this information, we can develop a personalized solution that meets the needs of customers and create a new way of advertising marketing. What you need to understand here is that it is not an accidental event to combine products and services through the analysis of big data. This is often the leader of the data age. In summary, the use of big data not only marks the progress of the times, but also inspires people to explore in a deeper field. In addition, in addition to the above, the research on big data requires understanding the three characteristics of big data, namely large scale, fast operation speed and data diversity. Through the study of these three aspects, not only can the nature of the data be more easily observed, but also beneficial to the effective operation of the software processing platform.
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Max. read speed up to 560 MB/s
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>SATA 2.5-inch Interface
Note
Capacity: 1GB=1,000,000,000 bytes by IDEMA. Performance test is performed in a specific testing environment. Any change of computer system, operation system, hardware, software, or functions will influence the test result.
Interface: 2.5", SATA 3 6Gb/s(compatible with SATA2 , SATA1,to drop speed )
Performance in the specifications is tested based on CrystalDiskMark.
Power consumption may differ according to flash configuration and platform. Power consumptions are measured by using CrystalDiskMark 1000 MB to test sequential R/W 5 times.
The TBW value is calculated based on Workload of JEDC 218B/219A standard.
The MTBF value is calculated based on the functional failure rate of JEDC 218B/219A standard.