What are the main aspects of big data security?

1. Scale, real-time and distributed processing

The essential characteristics of big data (make big data solve problems beyond those of previous data management systems) Data management and processing requirements (e.g., in terms of capacity, real-time, distributed architecture, and parallel processing) make it more difficult to secure these systems. Big data clusters are open and self-organizing and enable users to communicate with multiple data nodes simultaneously.

2. Embedded Security

In the crazy race involving big data, most development resources are used to improve the scalability and ease of use of big data. and analysis functions. Only few features are used to add safety features.

However, you want security features embedded into the big data platform. You want developers to be able to support the required functionality during the design and deployment phases. You want security capabilities to be as scalable, performant, and self-organizing as a big data cluster. The problem is that open source systems or most commercial systems generally don't include security products. And many security products cannot be embedded into Hadoop or other non-relational databases.

Most systems provide minimal security features, but not enough to cover all common threats. To a large extent, you need to build the security policy yourself.

3. Applications

Most applications for big data clusters are Web applications.

They leverage web-based technologies and stateless REST-based APIs. Web-based applications and APIs pose one of the most significant threats to these big data clusters. They can provide unrestricted access to data stored in big data clusters following an attack or breach.

Application security, user access management and authorization control are very important and are as indispensable as the security measures that focus on ensuring the security of big data clusters.

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