Data intelligence is a pivotal driver of the digital economy, fostering social innovation and high-quality economic growth. Mastering data intelligence equips enterprises with a competitive edge, enabling them to harness technologies, products, and theories to unlock the potential of internal data. The Enterprise Data Intelligence Platform, as a foundational infrastructure, integrates data storage, computation, analysis, and governance, offering end-to-end data life-cycle services. As digital transformation accelerates, building specialized and adaptive data intelligence platforms has become a critical industry focus. This paper explores the evolution, architecture, and implementation of enterprise data intelligence platform, providing insights for enterprise upgrades and envisioning future advancements in the field.

In recent years, with the breakthroughs in generative artificial intelligence technology, AI has become a key approach and effective means for unlocking the value of data elements. The relationship between the big data industry and the AI industry has evolved from a one-way empowerment to a deep integration, giving birth to the data intelligence industry. The collaboration between data and intelligence has together become the core force in transforming the physical world and reshaping the digital world order. The booming development of the data intelligence industry has become the core engine driving innovation and growth in the digital economy. This paper focuses on the topic of data intelligence. It systematically clarifies the technical system of data intelligence, conducts an in-depth analysis of the current status and issues in specific areas of data intelligence, such as data, algorithms, and applications, and also provides an outlook on the future development trends in various fields of the data intelligence industry.

The concept of "Lakehouse" was proposed by Databricks in 2020. Since "Lakehouse" was first written into Gartner’s Hype Cycle for Data Management in 2021, as a new technology, "Lakehouse" has received unprecedented attention from the enterprises who need digital transformation. More enterprises believe lakehouse is an important infrastructure for digital transformation. Currently, lakehouse is still in its early stage of development. It is not merely a technical research endeavor, but rather a gradual integration of technologies, representing a transitional phase in the evolution of heterogeneous data platform towards integration. This paper focuses on the lakehouse technology, sorts out the development history of the data platform and the practice path of lakehouse technology. It also lists main manufactures and products of lakehouse and provides the judgments for the future development of lakehouse.

With the advancement of digital transformation in various fields, the information industry has developed rapidly in the 21st century. Significant changes in system scale, research and development operation and maintenance models, technical architecture, and user groups have made the impact of information system failures wider and more severe. Disaster recovery systems can protect the data and applications of information systems before disasters occur, ensure the security of data in the event of a disaster, and achieve rapid business recovery. They have become an indispensable foundation for modern data infrastructure. This article provides a brief introduction to various disaster recovery technologies, followed by a summary of the principles that should be followed during the construction process of the disaster recovery system. Finally, it provides a detailed introduction to how to execute and manage the entire process of the disaster recovery system construction and proposes corresponding suggestions.

This paper reviewed current transactional database benchmark, especially TPC-C. There are some limitations to apply TPC-C into financial industry for benchmarking transactional database. In order to simulate real financial business, the project member has designed a benchmark named Databench-T, which is based on money transfer scenario. The model and workloads of the benchmark is introduced in detail in this paper. The benchmark is implemented by project members to provide a unified toolkit. To run a test, there are four steps: toolkit installation, data generation, workload execute and results display. All those steps are presented in the paper.

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.