The text discusses common data quality issues that arise from third-party data ingestion and data transformation processes, highlighting the challenges of managing data integrity in these contexts. It identifies third-party data as a major source of problems due to its unpredictable nature, such as changes in file formats, column names, and data values, which can lead to pipeline breakages and undetected errors. The text provides examples of how unexpected alterations in data structures can disrupt data pipelines, emphasizing the need for robust monitoring and alerting systems. Data transformation issues are also examined, with a focus on bugs resulting from incorrect technology use or misunderstood requirements, illustrating how these can lead to further data quality problems. The importance of unit testing and automated data quality monitoring tools is stressed as essential for detecting and addressing these issues, given the complexity and subtleties involved in maintaining data quality across diverse datasets.