Implementing continuous integration (CI) in data projects is increasingly critical due to the heightened emphasis on data quality, as it ensures rigorous validation of data and data pipeline changes to maintain integrity and prevent regressions. In the context of data, CI focuses on preventing untested or incorrect data from entering production, with the process involving distinct environments for development, staging, and production. For instance, a staging environment acts as a checkpoint to catch errors like incorrect data flags that could affect business operations, such as a recommendation engine in an e-commerce setting. This setup allows for automated testing and validation before code changes are deployed to production, thus safeguarding against potential disruptions. Data teams using tools like dbt are encouraged to reflect on their current practices and consider implementing a CI pipeline if they frequently face questions about the impact of code changes on data pipeline performance, adherence to quality standards, or the consistency of their testing processes.