dbt is a powerful framework that has become widely adopted by data organizations for building and managing data pipelines, yet as projects grow, certain challenges and limitations have emerged, such as increased warehouse costs and complexities with incremental models. Incremental models, which allow processing only new data, have introduced the challenge of ensuring timely refreshes, especially when changes affect upstream dependencies. Understanding both table-level and column-level lineage is crucial to efficiently manage these updates and avoid unnecessary full refreshes, which can be costly and time-consuming. Schema changes, particularly with incremental models, can lead to overlooked issues, emphasizing the importance of managing dependencies across multiple environments, especially when dbt projects interface with other tools like BI solutions. While these challenges are not inherently complex, they highlight the potential for simple code changes to cause significant disruptions, stressing the need for automated checks and technology solutions to enhance data quality and team productivity.