Four major security challenges facing large data development

In practical applications, "big data" seems to be often misunderstood. Large data does store and process a large number of data sets, but its characteristics are much more than that. Large data architectures and platforms are new things, and they are evolving at an extraordinary rate. The business and open source http://www.aliyun.com/zixun/aggregation/7434.html "> Development team is publishing its platform's new features almost every month.

Today's large data clusters will be very different from the data clusters we see in the future. Security tools to adapt to this new difficulty will also change. If security becomes an important requirement in the development of large data clusters, the cluster is not easily corrupted by hackers.

Large data phenomena are driven by the intersection of three major trends: large amounts of data containing valuable information, cheap computing resources, and almost free analysis tools. Today, there are many large data management systems that pay special attention to different data types. These systems use a variety of different query patterns, different data storage patterns, different task management and coordination, and different resource management tools. Although large data is often described as "inverse relational", the concept does not capture the nature of large data. To avoid performance problems, large data did discard the core functions of many relational databases, but did not make any mistakes: some large data environments provide relational structures, business continuity, and structured query processing.

Traditional definitions fail to capture the nature of large data, and consider large data based on key elements that make up a large data environment. These key elements use many distributed data storage and management nodes. These elements store multiple copies of data and make the data "fragmented" across multiple nodes. This means that when a single node fails, the data query shifts to processing the data available to the resource. It is this kind of distributed data node cluster that can cooperate with each other to solve the problem of data management and data query, which makes the big data so different. The loose connections of nodes bring many performance advantages, but they also pose unique security challenges.

Large data databases do not use a centralized "walled garden" pattern, and internal databases do not hide themselves and make other applications inaccessible. There is no "internal" concept, and large data does not depend on the point of concentration of data access. Large data exposes its schema to applications that use it, while clients communicate with many different nodes during the operation.

Embedded security: In crazy contests involving big data, most development resources are used to improve the scalability, ease of use, and analysis of large data. Only a few features are used to add security features. Most systems provide minimal security features, but not enough to cover all common threats. To a large extent, you need to build your own security policy.

Data security: Data stored in large data clusters is basically kept in a file. Each client application can maintain its own design of containing data, but this data is stored on a large number of nodes. The data stored in the cluster is vulnerable to all the threats that are susceptible to normal files, and the files need to be protected from illegal viewing and copying.

scale, real-time and distributed processing: The essential characteristics of large data make it more difficult to secure these systems. Large data clusters are open and self-organization, and enable users to communicate with multiple data nodes simultaneously. It is difficult to verify which data nodes and which customers should access the information. Don't forget, the nature of large data means that new nodes are automatically connected to the cluster, sharing data and query results, and solving customer tasks.

Applications: Most applications that target large data clusters are Web applications. They take advantage of web-based technology and stateless, rest-based APIs. While it is beyond the scope of this article to fully discuss the issue of large data security, web-based applications and APIs pose one of the most significant threats to these large data clusters. They can provide unrestricted access to data stored in large data clusters after they are attacked or compromised.