is becoming a new driving force for economic and social development, and is increasingly affecting economic operations, lifestyles and national governance capabilities. The security of big data transfer has been improved to the level of national security. Based on the challenges and problems facing big data transfer security and the development of big data security technology, we put forward the following 5 opinions for the development of big data security technology. 1. Build an integrated big data security defense system from the perspective of an overall security Security is a prerequisite for development. It is necessary to comprehensively improve the security of big data security technology, and then establish a comprehensive three-dimensional defense system that runs through the cloud management of big data applications to meet the needs of both countries. Big data strategy and it's market application. First, it is necessary to establish a security protection system covering the entire data life cycle, from collection to transfer, storage, processing, sharing, and final destruction. It is necessary to fully utilize data source verification, , encrypted storage in non-relational databases, privacy protection, data transaction security, prevention of data leakage, traceability, data destruction, and other technologies. The second is to enhance the security defense capabilities of the big data platform itself. It should introduce authentication for users and components, fine-grained access control, security audits for data operations, data desensitization, and other such privacy protection mechanisms. It is necessary to prevent unauthorized access to the system and data leakage while increasing attention to the inherent security risks involved in the configuration and operation of big data platform components. It is necessary to enhance the ability to respond to emergency security incidents that occur on the platform. Finally, it uses big data analysis, artificial intelligence, and other technologies to automatically identify threats, prevent risks and track attacks, and transition from passive defense to active detection. Ultimately, the goal is to enhance the security of big data from the bottom up and enhance the ability to defend against unknown threats. 2. Starting from attack defense, strengthen the security protection of big data platforms Platform security is the cornerstone of security. From an earlier analysis, we can see that the nature of cyberattacks against big data platforms is changing. Enterprises are facing increasingly serious security threats and challenges. Traditional defensive surveillance methods will find it difficult to keep up with this change in the threat landscape. In the future, research on the security technology of big data transfer platforms should not only solve operational security issues but also design innovative big data platform security protection systems to adapt to the changing nature of cyber attacks. In terms of security protection technology, both open source and commercial big data platforms are in a stage of rapid development. However, the cross-platform security mechanism still has shortcomings. At the same time, the development of new technologies and new applications will reveal platform security risks that are not yet known. These unknown risks require all parties in the industry to start from the offensive and defensive aspects, invest more in the security of the big data platform, and pay close attention to the development trend of big data network attacks and defense mechanisms. It is necessary to establish a defense system suitable for and build a more secure and reliable big data platform. 3. Use key links and technologies as breakthrough points to improve the data security technology system In the big data environment, data plays a value-added role, its application environment is becoming more and more complex, and all aspects of the data life cycle are facing new security requirements. Data collection and traceability have become prominent security risks, and cross-organizational data cooperation is extensive, leading to confidentiality protection requirements that trigger multi-source aggregate computing. At present, technologies such as sensitive data identification, data leakage protection, and database security protection are relatively mature, while confidentiality protection in multi-source computing, unstructured database security protection, data security early warning, emergency response, and traceability of data leakage incidents, Still relatively weak. Actively promote the development of industry-university-research integration, and accelerate the research and application of key technologies such as ciphertext calculations to improve computing efficiency. Enterprises should strengthen support for data collection, calculation, traceability, and other key links; strengthen data security monitoring, early warning, control, and emergency response capabilities; take data security key links and key technology research as a breakthrough; improve the big data security technology system; To promote the healthy development of the entire big data industry. 4. Strengthen the investment in the industrialization of privacy protection core technologies, while considering the two important priorities of data use and privacy protection In the big data application environment, data usage and privacy protection will naturally conflict. Homomorphic encryption, secure multi-party computing, and anonymization technologies can strike a balance between the two and are ideal technologies to solve the privacy challenges in the application of big data. The advancement of core privacy protection technologies will inevitably greatly promote the development of big data applications. Currently, the core problem of privacy protection technology is efficiency, and its problems include high computing costs, high storage requirements, and lack of evaluation standards. Some researches, in theory, have not been widely used in engineering practice. It is very difficult to deal with privacy security threats such as multiple data sources or statistics-based attacks. In the big data environment, personal privacy protection has become a topic of much concern, and with the increasing demand for privacy protection in the future, it will drive the development and industrial application of dedicated privacy protection technologies. To improve the level of privacy protection technology in the big data environment, we must encourage enterprises and scientific research institutions to study privacy protection algorithms such as homomorphic encryption and secure multi-party computing, and at the same time promote data desensitization, audit applications, and other technical methods. 5. Pay attention to the research and development of big data security review technology and build a third-party security review system At present, the state has formulated a series of major decision-making arrangements for big data security. The government promotes the deep integration of big data and the real economy and emphasizes the need to effectively protect national data security. The National Informatization Plan puts forward an implementation plan for the big data security project. It is foreseeable that the government's supervision of big data security will be further strengthened in the future, the legislative process related to data security will be further , big data security supervision measures and technical means will be further improved, and the disciplinary work of big data security supervision will be further strengthened.
The circulation of data has made some progress at the government level, especially since the 21st century, with the help of the power of science and technology, the collected data are more abundant, and the analysis based on these data can help decision-makers make more scientific decisions. For example, the city's mobile phone signaling data can accurately reflect the traffic congestion in each section of the city, and the traffic management department can optimize the traffic route accordingly. Data can help government departments make scientific decisions in advance and provide more convenient and comfortable services for residents. However, these big data managed by the government are heterogeneous, multi-source, massive, dynamic, and isolated, which also makes government agencies face severe challenges. Raysync simplifies the tedious IT management process for the government, and at the same time, ensures the security and controllability of documents. Ensure the security of data during transmission: Raysync adopts and latency, even the remote confidential cooperation of government departments and unreliable location information transmission can be safely and reliably completed. Accelerate inter-departmental cooperation to fully share the data of government agencies: support the rapid transmission of information across regions, and easily realize inter-departmental and inter-organizational cooperation. Make data easy to manage: government agencies have a large number of departments and users, and Raysync provides user management functions for management to realize hierarchical authority restrictions. A single user interface simplifies the transfer process between departments and various organizations.
In recent years, the increase in file size and number has brought challenges in file transfer. Firstly, files are transferred across borders, with serious packet loss and delay; secondly, it is also very difficult to transfer terabytes and petabytes of big data and move very large files. It requires users to spend hours or even days. The file must first be downloaded from the source system to its local desktop (if there is enough space). Only after success, can upload to the target server begin. Traditional methods are far from meeting the needs of enterprises for high-speed, complete, and secure file transfer. The performance and security of file transfer have become obstacles to the rapid development of many enterprises. The high-speed Internet connection can help increase file transfer speed, but high-speed Internet connection has a certain threshold. Advanced file sharing technology is required to process large files, that is, those files with time-sensitive information need to receive data as soon as possible. The latest report shows that from 2016 to 2023, the global hosted file transfer market is expected to grow at a compound annual growth rate of 6%. With this growth, cloud-based file transfer solutions will receive attention, is necessary for international companies to create a collaborative environment. It ensures that everyone can access the latest mission-critical information, whether in remote offices or branch offices. high-speed data transmission is developed using proprietary technology based on the User Data Protocol (UDP), which can increase file transmission speed and reduce data packet loss. Using the file transfer protocol can also optimize the transmission speed. The use of UDP-based technology can maximize bandwidth utilization and overcome the possible bandwidth limitations of other protocols (such as FTP and HTTP). In addition, intelligent routing technology is used to monitor network conditions and select the fastest transmission path with the highest success rate. This allows companies to simultaneously transfer large files to multiple global locations, 100 times faster than other methods.
The value of any organization's technology integration depends to a large extent on the quality of its for digital transformation machines. In short: big data can achieve digital transformation, anyway, this is the goal. So how can big data technology bring success to enterprises in the grand plan of things? It turns out that it is not as good as hope. Optimistic expectations for big data may exceed our ability to actually execute big data. The latest research on the UK online consulting and consulting platform shows that 70% of big data projects in the UK have failed. The study goes on to say that almost half of all organizations in the UK are trying to carry out some kind of big data project or plan. However, nearly 80% of these companies cannot fully process data. However, this is not news. About three years ago, Gartner, a leading research and consulting company, reported similar situations on a global scale and predicted that 60% of big data projects in 2017 would fail the early implementation stage. Worse, this forecast is too conservative, because 85% of big data projects that year ended up flat. So why do so many initiatives fail to meet expectations? When trying to drive value through big data projects, what measures can be taken to increase the likelihood of measurable success? The promise of big data, despite the fact that so many organizations are still working on big data projects, there are some reasons. Volume and speed——Data explosion: exponential data from more sources from increasing speed of creation Diversity——Mobile and IoT terminals, the proliferation of traditional data types and the massive increase in the amount of unstructured data Accuracy——As the saying goes: "Garbage in, garbage out." Big data projects are only as good as providing data. Value——The white rabbit of big data. Discovering influential insights or new value streams for the organization is the biggest challenge. It is a symbol of differences in potential income and competition. Value is the reason for entering big data in the first place. The continued potential of analytics and the prospect of deliverables have turned big data into a multi-billion dollar technology industry in less than a decade. This has a lot to do with McKinsey Global Institute’s 2011 bold prediction of big data: “Big data will become the key basis for competition, providing support for a new round of productivity growth, innovation, and consumer surplus, as long as there are correct policies. And the driving force is in place." The idea is that almost every company in every industry (retail, finance, insurance, healthcare, agriculture, etc.) is located in the , diverse, scattered, and disorganized enterprise data left in traditional systems and infrastructure. In the gold mine. Generated by a business. In order to obtain this treasure trove of information, each company needs specialized access and analysis tools to properly connect, organize, and ultimately transform it into a digestible and analyzable form. Assuming success, the big data infrastructure is expected to provide: Connect and unify all data sources Generate powerful business insights Allow predictive decisions Build a more efficient supply chain Provide a meaningful return on investment Comprehensively change every industry Although the potential of big data has been proven to be successful in many cases (mainly in large multinational companies and brands), the final state of big data required by most organizations has proven to be a difficult problem.
Many enterprises only need some instant messaging tools to handle the daily work, and they only need to accelerate the transfer of large data sets at certain specific times or situations. Some enterprises have only a handful of demands for the accelerated transfer of big data, and the traditional monthly or yearly plan will inevitably lead to unnecessary idleness and waste of resources. Raysync now supports pay-as-you-go service, and enterprises can start big data acceleration service by selecting corresponding traffic packets. The introduction of the traffic version can effectively control the cost consumption of enterprises, eliminate the waste of resources, and enjoy the accelerated transfer service of Raysync at low cost. The key features of Raysync Pay-as-you-go: Large file transfer: optimizes the transfer of large files by using the intelligent segmentation technology, supports the high-speed and stable transfer of large files, and break through the barriers of big data exchange. Configuration: 4-core 8GB memory; Bandwidth: 200MB/s; Test file: 10GB from Beijing to New York Mass small file transfer: supports intelligent compression, virtual splicing technology of mass small files, and new disk I/O optimization technology. ensures the integrity of transfer, supports the high-speed and stable transfer of mass small files, and makes mass data flow without barriers. Configuration: 4-core 8GB memory; Bandwidth: 200MB/s; Test file: 100,000 small files from Beijing to New York Transnational file transfer: Raysync self-developed high-speed transfer protocol allows the enterprises to send, share, and synchronize large data sets at high speed around the world. Synchronization function: Raysync has the flexible architecture and easy-to-construct data replication capability and supports bidirectional synchronization between server and client, that is, server files can be synchronized to client and client files can also be synchronized to the server, and synchronization function relieves users from the pain of manual copying. Raysync Pay-as-you-go provides efficient and controllable services of accelerating the transfer of large files, ultra-long distance and transnational network data transfer, and meets various transfer requirements of enterprises. Case study: An intelligent technology company urgently needs to transfer 8TB of R&D data to the Swiss branch, and there is no urgent need for big data transfer at ordinary times. Customer demand analysis: 1. "Urgent" demand: R&D data needs to be distributed to the Swiss branch as soon as possible and put into production immediately. If you mail the hard disk or the traditional FTP transfer method, you can't meet the customer's demand for the timely delivery of the data. 2. Cost control requirements: The companies’ has the characteristics of low frequency. The company has not set enough budget for the big data transfer project. After understanding the data transfer solutions in the market, it is found that a set of software needs to cost hundreds of thousands, which will cause serious waste of resources and financial resources, which is inconsistent with the needs of the company. 3. Flexible requirements: Just like the urgent transfer of 8TB files to Switzerland this time, we can't predict whether the next large file transfer task will also be affected by unexpected factors, that is, the transfer software pay-as-you-go plan is the best choice for enterprises. 4. Data security requirements: Production R&D data is the lifeblood of enterprise production and operation, and its importance is self-evident. In the process of transfer, due to poor network environment and long transfer distance, it may lead to low transfer efficiency, and file transfer security loopholes are more likely to occur. The leakage of R&D data will bring a devastating blow to enterprises. Considering the need to transmit enterprise R&D data overseas, the company has high requirements for the security of the transfer process. 5. Requirements for easy-to-use: The company's big data transfer frequency is low, the interface is simple, the operation is simple, and the convenient and fast transfer software is deployed, which will greatly reduce the time cost for employees to learn to use the software, and the work of data uploading and downloading will get started faster. The Raysync server is deployed at the Nanjing headquarters of the company, and the team members of the Swiss branch can log in to Raysync through IP address and port number, so as to easily upload and download the production R&D data. Compared with the traditional FTP transfer, the timeliness of Raysync transfer is improved by 96%. The values Raysync pay-as-you-go brings to customers: Raysync is based on UDP transfer protocol, which overcomes the difficulties of traditional data transfer software (such as FTP, HTTP), provides the highest transfer speed which is not affected by network conditions and transfer distance, delivers the important data assets in Switzerland at an extremely high speed, and puts them into tight production links to improve the competitiveness of enterprises. Bank standard encryption to ensure the safe delivery of important assets. Based on SSL encryption transfer protocol, technology of international top financial level is adapted to encrypt the data in the whole channel of receiving and sending, so as to ensure that the data will not be stolen or leaked during transfer and avoid the data assets of enterprises from being infringed. Enterprises can flexibly purchase the download traffic according to the actual situation, which can save 60% of the cost compared with other data acceleration transfer plans in the market. effectively controls the cost consumption of enterprises, eliminates the waste of resources, and realizes the cost optimization! It is simple to deploy and can be easily integrated into the existing system of the enterprise so that users can immediately obtain the big data transfer and control capabilities; It can flexibly set the group space and sub-account access rights, simplify the data delivery process, and reduce the learning cost of employees without changing the original operation mode.
In times of high speeds, the transfer of information is, perhaps, the fastest speed method. Indeed, it takes less than 1 second to send a picture to your friend. However, in order to send large amounts of data, fundamentally different solutions are needed. Modern business, if we are talking about developed enterprises with a lot of workers, is in dire need of prompt exchange of large amounts of information. Generally, the effectiveness of commercial processes as a whole may depend on the speed of obtaining and processing important information. Speed at all times was an indicator of progress. In ancient times, it took a lot of time and effort to migrate from place to place. Since the taming of animals, this process has begun to accelerate. Nowadays, the progress of high-speed transportation tools such as high-speed rail, freight trains, and freighters is constantly promoting the level of social productivity. If someone now asks to transport a few tons of goods from Beijing to Guangdong by carriage, the service provider is likely to say: "We do not deal with psychos!". Speed has become the indicator of the progress of human civilization. Moreover, the transfer of information flow also requires increasing data transfer speed to develop towards modernization. If we think deeper of history, we can recall that 25 years ago, the information exchange we saw was carried out with the help of fax, floppy disk, and laser disk. With the help of innovative technology, the speed of information exchange has achieved great progress unimaginable in previous centuries in this field in just 25 years. Centralized cloud services began to act as data storage, and such an "invisible warehouse" provided storage and receiving capabilities without using bulky and expensive physical media. But if we are talking about to deal with and transfer the important business information, the solutions mentioned above are actually useless. And the matter is not even in the absence of an adequate level of security of data transferred from public cloud storage, although this is also a very important aspect. Assume that some working servers of a large company need to be synchronized quickly to exchange information. If this operation is performed through the public cloud, it will take a lot of time for each server to download the relevant information. Especially for the financial, film and television, internet, and manufacturing industries, this method simply cannot meet the demand for . Relied on the self-developed Raysync high-speed transfer engine and Raysync Proxy protocol, supports the mainstream cloud storage platforms. With the 10 Gigabit bandwidth, it can satisfy all your TB-level large file transfer and massive small file transfer needs. Under normal circumstances, the server works as a whole, which can receive and distribute data, and automatically connect storage and transfer as a unified whole, which works together without affecting each other. In data security, and storage, which effectively promotes the business development of enterprises. As approaching of 5G era, as the vanguard of data acceleration, Raysync Transmission will always provide efficient, safe, and stable file transfer solutions for enterprises.
In the era of big data, the volume of data is growing day by day and the types of data are varied. As a result, big data interaction faces severe speed challenges. Returning to the essence, whether the transmission of TB-level large files and massive small files or both, network bandwidth utilization is the key to improve the data interaction efficiency. In order to ensure the rapid exchange of content, many enterprises spend a lot of money to improve the bandwidth, hoping that with the full use of network bandwidth, any network latency and packet loss will disappear. Although bandwidth improving is important in , there are so many ways to uitilize the network. There are many factors that affect the success rate and speed of transmission, the most important of which is to distinguish the difference between bandwidth and throughput. Bandwidth vs. Throughput Firstly, we need to know, network bandwidth isn't equate to network throughout. Available network bandwidth determines the potential maximum speed of data movement, while throughput is the actual speed of data movement. If files are moved in a long distance or crowded network environment without an appropriate software solution (for example, when using online file sharing tools such as FTP or Dropbox), throughput may be much lower than bandwidth, because the standard Internet transmission protocol TCP has obvious limitations. Upgrading the network without acceleration software is like moving from a country road to a highway while driving a scooter. If the transportation organization allows, it can drive at ultra-high speed, but the vehicle only keeps the same slow speed on a wider road. Indeed, many enterprises spends most costs in improving network bandwidth, but the actual transmission speed only get tiny imrovement, that made them depressed. How to solve the problem network bandwidth utilization? Internet enterprises may sometimes forget that purchasing more bandwidth is not the only way to speed up file transfer on the network or the Internet, and may not even be the best way. Choosing an effective solution has the same effect on the success rate and transmission speed of file transmission as enjoying private or public dedicated network. Facts have also proved that investing in the right file transfer solution is usually much better than spending money to upgrade bandwidth, after all, inferior transfer mechanisms may never fully utilize bandwidth. Of course, in the revolution of bandwidth utilization, it is always affected by multiple factors. Our newest customer case - Foxrenderfarm. Raysync Transmission helps it to solve the difficulty in transmission of the bulk of files, which explains the factors that affects the file transmission efficiency. With , your investment in greater bandwidth won’t go to waste. Our solutions ensure that bandwidth isn’t a conundrum anymore.
Data Transmission Service (DTS) is a data exchange service that streamlines data migration, data synchronization, and data subscription. DTS is capable of handling the most widely used commercial and open-sourced databases, including RDBMS (Relational Database Management System) and NoSQL. Data transmission is dedicated to solving the difficulties of data transfer over long distances and at high-speed in the public cloud and hybrid cloud scenarios. Here we will introduce you a successful case of data transfer related to Huawei. Huawei is headquartered in Shenzhen and has so many branches scattered around the world, thus the data and file exchange is very common. Chinasoft International Ltd., Huawei's partner in Shenzhen, needs to download videos from the German source video storage center of Huawei, transcode and re-process those videos, and then transfer the completed files to the German finished video storage center. There are some problems such as unstable transnational data transmission, the low utilization rate of transmission bandwidth, low security of data transmission, etc. As a professional solution provider of large file transmission, Raysync Transmission cooperated with Huawei to solve the problems in file transmission. Raysync Transmission helped to deploy the Raysync server in Germany’s Linux host and the web client in Chinasoft’s Windows host. After transcoding the videos downloaded from Germany, Chinasoft then transferred them to Germany through Raysync Transmission. In the process, Huawei was helped to improve the bandwidth efficiency and work efficiency to the greatest extent, and the transmission speed of the project was increased by tens of times.
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