A data warehouse efficiently prepares data for effective and fast data analysis and modelling using machine learning algorithms. This paper discusses existing solutions for the Data Extraction, Transformation, and Loading (ETL) process and automation for algorithmic trading algorithms. Integrating the Data Warehouses and, in the future, the Data Lakes with the Machine Learning Algorithms gives enormous opportunities in research when performance and data processing time become critical non-functional requirements.

翻译：数据仓库利用机器学习算法高效地准备数据，以支持快速有效的数据分析与建模。本文探讨了针对算法交易算法的数据提取、转换与加载（ETL）流程及自动化的现有解决方案。当数据仓库（未来包括数据湖）与机器学习算法集成时，若性能与数据处理时间成为关键的非功能性需求，将为研究带来巨大机遇。