Many data teams face challenges when managing local, test, and production environments. A broken data pipeline can lead to corrupted records, making quick rollbacks essential. The article introduces a Git-like approach for data management, allowing teams to branch, test, and deploy data similarly to how they handle code. The primary benefit is the ability to efficiently manage changes across complex data architectures without the extensive time and resource requirements typical of traditional methods. The article dives into the limitations of using standard Git for data. Git excels with code but struggles with large datasets and binary files. It lacks features like cell-level conflict resolution and schema management, making it unsuitable for data versioning. Instead, specialized tools like LakeFS and Nessie aim to provide the Git-like functionality necessary for data management. These tools enable branching and versioning in a way that aligns better with the needs of data engineers. The goal is to integrate Git principles into data workflows to streamline testing and deployment. By applying concepts like branching and versioning, data teams can better manage production data and handle errors more effectively. The article emphasizes the importance of finding efficient solutions for scaling Git-like workflows in large production environments, as copying vast datasets can be impractical and time-consuming. Exploring these tools and workflows could significantly improve data management processes.