Big Data Security Challenges

The big data architecture and platform are new things and are still developing at an extraordinary speed. Commercial and open-source development teams release new features on their platforms almost every month. Today's big data clusters will be significantly different from the data clusters we will see in the future. The new and difficult security tools will also change. The industry is still in its early stages in adopting the big data lifecycle, but the sooner the company starts to deal with the security issues of big data, the easier the task will be. If security becomes an important requirement in the development of big data clusters, the clusters will not be easily damaged by hackers. In addition, companies can avoid putting immature security functions in critical production environments. The word "Big Data" is often misunderstood. In fact, the usage frequency is too high, which makes it almost meaningless. Big Data does store and process a large number of data sets,However, its features are far more than that.When starting to solve the big data problem, it is very helpful to regard it as an idea rather than a specific scale or technology. As far as its simplest performance is concerned, the big data phenomenon is driven by the intersection of three major trends: a large amount of data containing valuable information, cheap computing resources, and almost free analysis tools. Nowadays, there are many big data management systems that place special emphasis on different data types (such as geographic location data. These systems use a variety of different query modes, different data storage modes, different task management and coordination, and different resource management tools. Although big data is often described as "anti-relational", this concept cannot grasp the essence of big data. To avoid performance problems, big data does discard the core functions of many relational databases, but has not made any mistake: Some Big Data Environments provide relational structures, business continuity, and structured query processing. Because traditional definitions cannot grasp the essence of big data, we may consider big data based on the key elements that constitute the big data environment. These key elements use many distributed data storage and management nodes. These elements store multiple data copies and convert data into fragments between multiple nodes ". This means that when a single node fails, data query will turn to processing available data. This distributed data node cluster that can collaborate with each other can solve data management and data query problems, which makes big data so different. The architecture diagram of a Hadoop file system shows how data nodes interact with clients. The loose connection of nodes brings many performance advantages, but also brings unique security challenges. Big Data databases do not use a centralized "wall garden" Model (compared with the "completely open" Internet, it refers to an environment that controls users' access to webpage content or related services. internal databases do not hide themselves, and other applications cannot access them. There is no "internal" concept here, and big data does not rely on centralized data access points. Big Data exposes its architecture to applications that use it, while the client communicates with many different nodes during the operation.

Scale, real-time, and distributed processing:Essential Features of big data (enable big data to meet data management and processing requirements of previous data management systems, such as capacity, real-time performance, distributed architecture, and parallel processing) this makes it more difficult to ensure the security of these systems. Big Data clusters are open and self-organized, and allow users to communicate with multiple data nodes at the same time. It is difficult to verify which data nodes and which customers should access information. Don't forget, the essential attribute of big data means that new nodes are automatically connected to the cluster, sharing data and query results to solve customer tasks.

Embedded Security: In the crazy competition involving big data, most development resources are used to improve the scalability, ease of use, and analysis functions of big data. Only a few features are used to add security features. However, you want to obtain the security functions embedded in the big data platform. You want developers to support the required features in the design and deployment phases. You want security functions to be upgraded, high-performance, and self-organized like Big Data clusters. The problem is that open-source systems or most commercial systems generally do not include security products. In addition, many security products cannot be embedded into Hadoop or other non-relational databases. Most systems provide the least security functions, but not all common threats. To a large extent, you need to build your own security policies.

Application:Most applications oriented to Big Data clusters are Web applications. They use Web-based technologies and stateless REST-based APIs. Although the comprehensive discussion of big data security is beyond the scope of this article, Web-based applications and APIs pose the most significant threat to these big data clusters. After being attacked or damaged, they can provide unrestricted access to the data stored in the big data cluster. Application Security, user access management, and authorization control are very important, just like security measures that focus on big data cluster security.

Data security:The data stored in the big data cluster is basically stored in files. Each client application can maintain its own Design of Data inclusion, but such data is stored on a large number of nodes. The data stored in the cluster is vulnerable to all threats that normal files are vulnerable to infection. Therefore, you must protect these files to avoid unauthorized viewing and copying.