Alibaba Cloud Cloud Native Integrated Data Warehouse Released
Xuefeng Gao, General Manager of Alibaba Cloud Computing Platform Products and Solutions
Cloud Native Integrated Data :Market Background
Cloud-Native Integrated Data, Last year, the global big data software market size has exceeded 500 billion yuan, and it has maintained a compound growth rate of more than 10%. China is one of the countries and markets with the largest amount of data and data scale. Last year, the scale of big data-related software exceeded 10 billion yuan, with a year-on-year growth rate of nearly 40%, and the expected compound growth rate in the next few years will exceed 30%.
IDC's report clearly pointed out that Alibaba Cloud's market share of big data public cloud in the first half of 2021 is 43.5%, ranking at the forefront of China's major big data service providers. The scale of Alibaba Cloud's big data-related industries is even larger. The overall market size of hardware and services driven by big data software in China is expected to exceed 3 trillion in 2025, and the compound growth rate will remain at around 25%.
China's 14th Five-Year Plan also clearly mentioned that to speed up the high-value transformation of data, the following conditions must be achieved:
1. Cloud-Native Integrated Data, Large-scale data aggregation, data collection in all links, and the construction of industrial-based big data.
Diversified data processing, including data processing of various data types, multimodalities, and multi-industry.
Time-sensitive data flow, including a dynamic update of data, the establishment of data sharing space, etc.
High-quality data governance, well-managed data assets, and full life cycle.
High-value data transformation, including government governance, social governance, risk control, industrial upgrading, and financial technology upgrading through data.
Cloud-Native Integrated Data, Big data has more and more mature applications in different industries.
The application scenarios in the Internet industry are basically the construction of data warehouses driven by business indicators. The core of the goal is to improve operating efficiency, reduce operating costs, and promote user growth through promotion. In the financial industry and traditional government and enterprise industries, there are also It has invested in the construction of the data center, and gradually shifted from the original steady-state IT infrastructure to a dual-mode IT architecture driven by data-driven agile and steady-state IT, and began to build the core for the intelligent upgrade of the entire industry chain. data infrastructure.
Cloud-Native Integrated Data, The national plan also clearly stated that we should cultivate professional and scenario-based big data solutions, build a multi-level industrial Internet platform, and build a big data platform for the industry.
For example, in raw material enterprises, how do promote the processes? In manufacturing-related enterprises, how to promote the data flow of the full value data chain to truly realize data-driven? How do achieve accurate supply and demand docking in the consumer goods industry? In communications, finance, medical care, emergency response, agriculture, water conservancy, public security, transportation, electric power, and many other industries, how to build an industry data platform to promote the overall industrialization upgrade? These are the places where big data can really play its core value in various industries.
Cloud-Native Integrated Data, At this stage, various industries and industries are using the capabilities of big data for industrial upgrading, which also puts forward more and higher requirements for the platform that carries the basic big data for the entire data analysis.
First, the types of data are becoming more and more abundant. On the basis of the original BI analysis of structured data, more and more enterprises have begun to have the need to analyze semi-structured and unstructured data. How to combine the technology of data lake and the mature data governance modeling experience of the data warehouse to bring a better ability to analyze diversified data for enterprises has become the core issue of many enterprises.
Secondly, cloud-Native Integrated Data, more and more enterprises have encountered the problem of data silos. Each department has its own data platform, but the collaborative analysis of data is also a very basic rigid requirement. Alibaba once took 18 months to bring together different data into a unified data platform, but this is difficult for most enterprises to achieve.
Therefore, how to make good use of the existing separate data platforms of enterprises to conduct unified data management, data governance and data value mining have also become a very important assessment point for data platform providers.
Cloud-Native Integrated Data Previously, the demand for data analysis of many enterprises was to be able to produce a very rich report in a few days, that is, traditional offline big data analysis. However, as business competition becomes more and more intense, the timeliness of data analysis to guide business decisions is becoming more and more demanding. In many cases, it is necessary to perform real-time analysis on data and combine some offline data analysis results to provide real-time analysis for business decision-making. Therefore, the timeliness of analysis, the corresponding cost performance, and the consistency of the final data analysis results have also begun to be paid attention to by more and more enterprises.
With the prosperity of the big data market, more and more analysis engines have emerged, and the technologies of data lakes and data warehouses have also been iterated and developed to varying degrees. In order to ensure the effectiveness of the value generated by data analysis, enterprise data quality, and standards as well as data management and governance have been concerned by customers in various industries. It is necessary to prevent the garbage in and garbage out of data to improve the density and effectiveness of data value mining. Therefore, the data platform must have data management and governance capabilities.
In addition, cloud-Native integrated data, the value of the data analysis life cycle has also been paid more and more attention by more and more enterprises. Not only the cost of building a data platform, but also the cost of hardware investment in the early stage, the cost of complex operation and maintenance in the later stage, the cost of software R&D and application development, and the emergency cost brought by the company's response to sudden business growth are all basic for companies to build a big data analysis platform. cost.
We will release the Alibaba Cloud Integrated Data Warehouse, combining with Alibaba Cloud's big data star products - MaxCompute, Hologres and DataWorks, to solve many pain points in all walks of life through a one-stop big data analysis platform:
① Through MaxCompute and The deep integration of Hologres and Alibaba Cloud integrated data warehouse provide rich and flexible off-line/online integration capabilities.
②Through more open support for data lakes and diversification of data analysis, it provides the ability to integrate lakes and warehouses under unified management.
③Based on a piece of data, constantly pursue the combination of real-time and online capabilities of data warehouses.
④ Through the top-down and bottom-up bidirectional modeling capabilities of DataWorks and the new capabilities of data governance and enterprise data maturity assessment models, it helps enterprises to feel their own data maturity more intuitively. The open DataWorks plug-in system also allows Alibaba Cloud's customers and industry SVs to build more scene-based data analysis capabilities around their own big data, thereby truly helping to upgrade their businesses intelligently.
In short, Alibaba Cloud Integrated Data Warehouse will meet the ever-increasing expectations of enterprises for the capabilities of big data analysis platforms through offline/online integration, lake and warehouse integration, analysis service integration, and full-link data governance capabilities.
Knowledge Base Team
Knowledge Base Team
Knowledge Base Team
Knowledge Base Team
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