Integration Methods and Advantages of Machine Learning with Cloud Data Warehouses

Hanzhe Li; Xiangxiang Wang; Yuan Feng; Yaqian Qi; Jingxiao Tian

doi:10.62051/ijcsit.v2n1.36

Authors

Hanzhe Li
Xiangxiang Wang
Yuan Feng
Yaqian Qi
Jingxiao Tian

DOI:

https://doi.org/10.62051/ijcsit.v2n1.36

Keywords:

Data Warehouse; Cloud Data Warehousing; Machine Learning Integratio; Parallel Integration Methods

Abstract

A data warehouse is a subject-oriented, integrated, relatively stable collection of data that reflects historical changes and is used to support management decisions. Common tools for building a data warehouse are IBM Cognos and SAP BO. However, both of the above use centralized single-node mode to build data warehouses. This type of data warehouse has poor scalability, and due to the rapid increase in the scale of the Internet, traditional data warehouses can no longer meet the actual needs of use. This paper mainly introduces the integration of cloud data warehouse and machine learning as well as the importance and application of parallel integration methods. First, the paper describes how the combination of cloud data warehousing and machine learning can promote business innovation and increase output. It then discusses the challenges of managing machine learning models in production environments, and introduces the role of cloud data warehouses in addressing these challenges. Subsequently, the cloud computing integration of Snowflake, as well as the implementation steps and processes of the parallel integration approach are also introduced in detail. Finally, the results of parallel integration method are analyzed, and it is considered that it has a good application prospect and development potential in cloud data warehouse.

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