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Even-numbered technology releases fourth-generation data warehouse products fit perfectly with AI applications

Once upon a time, "traffic is king", traffic is the core competitiveness of Internet enterprises. As the Internet in China has entered the data age, the major Internet enterprises have gradually changed from the competition for traffic entry to the competition for data entry. How to manage and analyze data efficiently has become the core of more and more Internet enterprises.

Dr. Chang Lei, even-numbered technology CEO, said in a recent interview with 51CTO that the new generation of databases must correspond to the brain level of AI and match the application scenarios of artificial intelligence. only in this way can we comply with the law of technological development and seize the opportunity of the data era. Thanks to its sensitivity and foresight to technological development, even-numbered technology has been favored by Redpoint Ventures and Sequoia Capital, and has received financing from both. The evolution of database

Before we talk about the new generation of databases, let's briefly review the development history of databases. During the decades of the development of database system, it has experienced the first generation of shared storage database, the second generation MPP database and the third generation database with storage and computing separation architecture.

Shared storage database, in order to save resources and reduce developer costs, there is a shared storage database, different users can use the data in the database according to their own usage; multiple users can share the data resources in the database at the same time, that is, different users can access the same data in the database at the same time. Traditional Oracle database belongs to shared storage database, and it is also the earliest transactional database.

MPP database is currently used by a large number of companies, including Teradata and Vertica. Because the underlying foundation such as operating system and file system is not very mature, Teradata uses proprietary hardware, focuses on hardware and software, and changes a lot of operating system and file system code. Around 2000, MPP databases based on x86 architecture appeared, including Vertica,Greenplum and so on. These MPP databases based on x86 architecture use ordinary servers and are not supported by proprietary hardware, so the software architecture is still similar to Teradata.

Storage and computing are separated from architectural databases, and typical representatives of such databases are HAWQ and Hive. The advantage of the third-generation data warehouse is good scalability, but most of the engines such as Hive have poor performance and compatibility, so it is difficult for customers to use.

New Generation Analytical Database of HAWQ under artificial Intelligence scenario

Dr. CEO Chang of even number Technology said that traditional analytical databases have certain advantages in exploring the value of information data, such as online statistics, online analysis, and random query. However, in the artificial intelligence scenario, in the face of the drawbacks of massive data analysis, both the feedback time and the stringent requirements of equipment performance have been unable to meet the requirements of artificial intelligence scenarios. Therefore, it is urgent to iteratively upgrade the database.

Under this background, the analytical database based on HAWQ, which is built by even technology, arises at the historic moment, and has a unique advantage in the massive data processing of artificial intelligence.

The fourth Generation Analytical Database solves the Core problems of Multi-domain data processing

With the endless emergence of big data and artificial intelligence applications, higher requirements are put forward for the massive data processing and analysis ability of the database. Whether it is security, finance or manufacturing, there is a very urgent need to improve the performance of the database, and with the birth of the fourth generation of analytical database, this problem is easily solved.

In the application scenarios of artificial intelligence, such as the field of security, the fourth generation database can carry out deeper cognition and reasoning through image recognition, face recognition and so on. In the aspects of personnel identification and human trajectory analysis, we can quickly analyze, collide, discover potential connections, and extract valuable information from different kinds of data, such as videos, pictures, electronic license plates, and so on. and issued an early warning of suspicious behavior to correlate the case to help improve the efficiency of the detection of cases in the public security department.

For real-time data and other practical cases, such as millions or even tens of millions of real-time Internet of things sensor data within the enterprise, the fourth generation database completely abandoned the tedious Storm and other schemes, and solved the problem of a large number of programming and can not fully adapt to the Internet of things sensor data.

In the financial field, banks originally need to check the enterprises and then evaluate them through the jury, which requires full manual operation. If it is a small and micro loan, because the information is easier to obtain, it is less difficult. Once the business of the company is involved, the bank needs to integrate all the external and internal data of the enterprise according to the theme, which is not only time-consuming and laborious, but also brings great difficulty to manual analysis.

With the technical advantages of the fourth generation database, even technology can use data storage, management, analysis and artificial intelligence algorithm modeling, easily help customers to build model processes and model management and online, simplify the previous complicated audit process, less manual participation, shorten the audit time.

The Future of the fourth Generation Analytical Database

Chang Lei believes that there is a huge demand for data mining not only in the three areas just mentioned, but also in many basic fields, such as electric power, communications and so on. At the same time, the great change of database application demand indicates that a great change is coming in the database field.

At this turning point, even number Technology launched two core products, Oushu Database and LittleBoy, both of which are based on the industry-leading HAWQ technology research and development. The former will meet the basic needs of database upgrade, while the latter will push the database technology to the future of AI artificial intelligence applications.

At the end of the interview, Dr. Chang Lei said, "the 11th issue of Microsoft Accelerator in Beijing is of great help to even technology." On the one hand, Microsoft Accelerator Phase 11 Beijing and even Technology have reached some cooperation in research and development, and even number Technology has transplanted its products to the Azure public cloud in Microsoft Accelerator Phase 11 Beijing; on the other hand, Microsoft Accelerator Phase 11 Beijing has introduced a lot of customer resources from different industries, and training in management, finance, recruitment, PR and other areas has also provided guidance on even number technology. "

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