After more than 30 years of development, China's security industry has evolved with technological advancements, leaving its mark on the times. Clearly, China's security industry has entered the big data era, leading to constant related discussions.

　　Constraints Faced by Storage in the Big Data Era

　　Video surveillance is a typical data-dependent business that relies on data. Big data and video surveillance have a natural synergy. Big data often reaches petabyte-level scales, demanding high scalability from massive data storage systems. If we consider the hundreds of millions of cameras in China's Safe City projects, video surveillance becomes a major data generator. However, there's a contradiction between the massive amount of raw data from the front-end cameras and the amount of useful data. Cameras continuously record everything within their range, but much of this information is irrelevant to the customer. Useful information may only be present for short periods, putting significant pressure on databases and wasting resources. Therefore, storage becomes a crucial issue in the big data era. For example, in early 2015, Nanjing police mobilized 2,000 officers to apprehend a fugitive. Because cameras weren't networked, they had to retrieve data from various communities, police stations, and buildings, buying almost all available storage devices in Nanjing. This case illustrates three points: First, data needs to be networked; second, big data must be stored properly; and third, big data must be analyzed and mined to be valuable.

　　The simple statement that big data must be stored properly highlights the importance of storage in the big data era for security. Generally, big data surveillance storage faces several constraints.

　　How to store massive amounts of information. Surveillance data is written 24/7, stored for 7, 15, 30 days, or even longer. The data volume increases linearly over time. Traditional storage and surveillance industries struggle to handle this massive storage demand.

　　Scalability of surveillance data storage, including how to meet the needs of higher resolution data acquisition or more acquisition points, and how to meet the needs of longer data acquisition times.

　　Performance requirements. Video surveillance mainly involves writing video streams. Performance is characterized by the number of streams the storage can support. Concurrent writes significantly impact bandwidth, data capacity, and caching, putting a lot of pressure on storage. Storage needs specialized optimization for video performance.

　　Price sensitivity. The massive storage in the security surveillance industry leads to high overall costs. This means a high demand for low cost per unit capacity (price per TB).

　　Space pressure and management difficulty. With the increasing prevalence of high-definition video, the demands on backend storage are rapidly increasing. The resulting two-to-three-fold increase in data volume puts unexpected pressure on traditional PC hard drive management or local DVR/NVR modes.

　　Centralized management of stored data needs improvement. Analyzing the overall structure of the surveillance system in the big data era reveals issues such as incompatibility of storage devices. Large surveillance systems are often built in phases, using different devices. This leads to difficulties in centralized management of storage from various brands and models. Traditional DVR or DVS devices have limited network transmission capabilities, making it difficult to form a unified storage and monitoring center architecture. This results in problems such as difficulty finding stored data, untimely dispatch, and scattered video storage.

　　How to overcome these constraints?

　　Cloud Storage Meets the Big Data Era

　　Cloud storage for surveillance overcomes the performance and capacity bottlenecks of traditional storage methods, meeting the needs of the new era. Cloud storage providers can connect numerous different types of storage devices to create exceptionally powerful storage capabilities, achieving linear scalability of performance and capacity. This makes storing massive amounts of data possible, giving businesses the equivalent of cloud-scale storage and solving the big data storage problem.

　　The so-called "cloud model" is a figurative term for the data center model. After years of development in the IT field, the core idea of the data center model is that information operations can be broken down into segments, distributed using network connections, and flexibly allocated between local and remote locations. Almost all large IT systems are now data-center based, and the internet itself provides the basic physical infrastructure for data centers. Cloud storage is a new concept extending from cloud computing. It uses cluster applications, grid technology, or distributed file systems to combine numerous different types of storage devices into a system that provides data storage and access services. When the core of a cloud computing system is the storage and management of large amounts of data, it needs to configure a large number of storage devices, transforming it into a cloud storage system. Therefore, cloud storage is a cloud computing system focused on data storage and management.

　　When discussing advanced storage technologies, cloud storage must be mentioned. It's a very attractive technology that enables complete storage virtualization, simplifying applications, saving customer costs, and providing stronger storage and sharing capabilities. All devices in cloud storage are completely transparent to the user. Any authorized user can connect to cloud storage from anywhere with a single connection line to access space and data. Users don't need to worry about storage device models, quantities, network structure, storage protocols, application interfaces, etc. The application is simple and transparent. For users, cloud storage isn't a specific device but a collection of many storage devices and servers. Users don't use a specific storage device but the data access service provided by the entire cloud storage system. Strictly speaking, cloud storage isn't storage but a service.

　　The core of cloud storage is the combination of application software and storage devices, using application software to transform storage devices into storage services. Compared to traditional storage devices, cloud storage is not just hardware but a complex system consisting of network devices, storage devices, servers, application software, public access interfaces, access networks, and client programs. The storage devices are at the core, providing data storage and access services through application software. Cloud storage is more about applications. Application storage is a storage device that integrates application software functions. It not only has data storage functions but also application software functions, and can be considered a combination of server and storage device. The development of application storage technology can greatly reduce the number of servers in cloud storage, reducing system construction costs, reducing single points of failure and performance bottlenecks caused by servers, reducing data transmission, improving system performance and efficiency, and ensuring the efficient and stable operation of the entire system.

　　Conclusion

　　In the big data era, the widespread deployment of surveillance cameras has led to enterprises needing to store massive amounts of video files, severely occupying private storage space. This has spurred the demand for cloud storage in the video surveillance industry. Cloud storage not only saves enterprises' private storage space by storing video files in the cloud, but also facilitates cross-regional video searches through centralized cloud storage. Beyond cloud storage, currently popular big data technologies also provide more technical support for intelligent analysis of video data in the video surveillance industry.

　　It can be said that cloud storage makes big data "small" and security "big".