This document discusses applying chaos engineering principles to complex data systems. It recommends defining a steady state, acknowledging real-world events, running manual experiments in production, and automating production experiments. This helps discover weaknesses and manage the data lifecycle through stages like development, experimentation, deployment, and production. It also presents the idea of using a "Git for Data" system like lakeFS to version data and enable features like branching, rolling back, and atomic updates to more easily develop, test, and recover from errors in data pipelines.