The integrated big data platform aims to provide one-stop development and operation capabilities for massive heterogeneous data. Various components are organically combined and managed in a unified way. At the same time, the underlying technical details are shielded from users, so as to finally achieve the purpose of convenient data development, reducing operation and maintenance costs, and standardizing management processes. This article introduces the functional modules that the platform should have one by one, covering data storage, data integration, data management and governance, data development, data processing and analysis, high availability, operation and maintenance management, and data security. Finally, the typical characteristics and development direction of the integrated big data platform are pointed out.

With the continuous emergence of cloud computing technology, cloud infrastructure software will become the mainstream application model in the future. Among the databases, relational databases occupy the largest market share. Therefore, the relational cloud database will be the main product of the combination of database technology and cloud computing technology, and will become an important branch of the database industry. This article explores the establishment of an evaluation system framework for relational databases, helping enterprises to select relational cloud database products according to a clear goal and path. This article can help enterprises complete the landing of relational cloud database projects.

In industry, analytic database is known to be difficult in testing and product selection. The traditional TPC-DS (transaction processing performance council-decisioin support) benchmark [1], which only has one retail scene, is not sufficient enough for database product selection of other business type. To deal with this drawback, an analytical database testing platform which can plug-in a variety of business scenes was developed. The telecommunication scene was also provided to explain the design concept, system architecture and scene development of this testing platform. The platform provides scene plug-in function which is easy to use without any programming background. Users can select and edit scenes according to their own business type to meet the requirements of analytical database testing and selection in different industries.

At present, data security has become a key area of attention from all aspects. The proliferation of data leakage incidents, the acceleration of the introduction of data security laws and regulations, and the rising cost of enterprise compliance punishment jointly urge all enterprises to step up to improve their own data security system. In the construction of data security, data desensitization is always a core technical ability. In view of the current situation and future development trend of data desensitization technology and products, this paper introduces the development of data desensitization technology in detail from desensitization algorithm, anonymization, static data desensitization and dynamic data desensitization. At the same time, from the core function, product form, supply side status and other aspects of the data desensitization technology are described. On this basis, the standardization direction of data desensitization products in terms of core functions, concept definition, performance and stability is proposed. Finally, the development trend of data desensitization technology and products in four aspects of regularization, anonymization, intellectualization and contextualization are put forward.