What challenges might one encounter while using DTS for data integration?

Data transformation errors are a significant challenge when using DTS for data integration. When integrating data from multiple sources, it's crucial to accurately transform the data into a format that is compatible with the target system. This process involves manipulating, converting, and reformatting the data, which can lead to errors if not handled correctly. These errors might occur due to various reasons such as incorrect mapping of fields, incompatible data types, or misunderstanding of the data workflow.

Such errors can compromise data integrity and may result in loss of important information, incorrect analysis, or system failures. Moreover, addressing these transformation issues often requires additional time and troubleshooting efforts, further complicating the data integration process.

In contrast, while limited data types, low computer memory, and high maintenance costs can pose challenges, they tend to be less variable and harder to impact directly during the data transformation phase. Data transformation errors are ongoing risks that can manifest as new data sources or changing requirements emerge, highlighting the need for careful planning and validation during the data integration process.