This systematic review explores best practices in data transformation for streamlined data warehousing and analytics. As organizations continue to generate vast amounts of data, transforming and warehousing it efficiently becomes critical to support data-driven decision-making. The review discusses key practices, including the automation of data transformation processes, ensuring data quality and integrity, and optimizing scalability and performance. Automation, particularly through ETL/ELT tools, reduces manual errors and enhances operational efficiency, while data quality practices ensure reliable and accurate analytics. Scalability and performance optimization techniques, such as leveraging cloud-based solutions and parallel processing, are vital for handling growing data volumes. Furthermore, emerging technologies like artificial intelligence (AI), machine learning (ML), and real-time data transformation are revolutionizing data transformation processes, enabling faster, smarter, and more dynamic data workflows. This review concludes with implications for the future of data warehousing, highlighting the role of automation, AI/ML, and real-time processing in shaping future data strategies. Practical recommendations for practitioners and researchers are provided, emphasizing the integration of these best practices for more effective and agile data management.