Abstract: Transferring data across storage types, formats, including computer systems is data migration. It crucial for system installation, upgrading, and consolidation. Due to various business demands, numerous sectors have prioritized it. The ETL procedure is crucial to data warehouse construction. Data is extracted from different operation kinds and loaded into a data warehouse in diverse contexts using numerous technological methods. This method combines data from diverse sources and operation kinds and converts nonstandard data into standard ones. Systems and techniques may analyze an underlying language structure within the source data integration framework to establish logical syntax. The rapid growth of data-driven decision-making in modern enterprises has increased the reliance on Extract, Transform, and Load (ETL) processes and Data Warehousing systems. These components form the backbone of analytical and business intelligence operations by integrating, cleaning, and consolidating data from multiple sources into a unified repository. However, as the volume and complexity of data increase, so do the risks associated with unauthorized access, data leakage, and system vulnerabilities. This paper presents a comprehensive overview of the architecture of ETL and Data Warehousing systems, highlighting the key stages, workflows, and technologies involved. It further examines common security vulnerabilities, including data breaches, insider threats, injection attacks, and configuration weaknesses that compromise data integrity and confidentiality. Finally, the study explores security mechanisms and best practices such as encryption, authentication, access control, auditing, and secure ETL pipeline design to mitigate these threats. The analysis emphasizes the importance of embedding security measures throughout the data lifecycle to ensure trustworthy and resilient data warehousing environments.