Continuous Evolution and Development of Data Warehouse Architecture

Challenges Faced by Traditional Data Warehouses

  1. First, we hope to have a unified data platform that can connect, store and process a variety of data.
  2. Second, enterprises are data-driven to make support and decision-making information in real time, which requires higher timeliness.
  3. Third, the data volume has become very large. To find the desired data from such a large amount of data, a map is required for data management and governance.
  4. Fourth, in a traditional data warehouse, data is stored in a centralized manner, and the data must be stored in the same storage. Driven by new businesses, data needs to be connected instead of stored in a unified manner.
  5. Fifth, how to support more intelligent applications on top of the data warehouse, the information-based services and the business informatization. This is the driving force for intelligent data warehouses.
  6. Sixth, different roles in the data field have different requirements for data platforms. For example, data engineers, data analysts, and data scientists have different requirements for data platforms in terms of response time, processing speed, data volume, and development languages. Therefore, providing more analytics services is the sixth driver for data management platforms.

Cloud Native — The Evolution Direction for Data Warehouse Infrastructure

Lake House: The Evolution Direction for the Data Architecture of Data Warehouse

Offline-Realtime Unification — The Evolution Direction for Data Analysis of Data Warehouse

SaaS Mode — Evolution Direction for Data Warehouse Service Model


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