In restricted or isolated network environments, Dataflow workers often cannot reach the public internet to download runtime dependencies. To operate securely, organizations build custom worker images that bundle required libraries. However, these images must be manually updated to keep dependencies current. As upstream packages evolve, outdated internal images can cause pipeline errors, execution delays, or total job failures. Each failure wastes worker runtime, increases troubleshooting time, and leads to rebuild cycles that inflate operational and compute costs.
Dataflow billing is based on worker instance time, storage, and additional data transfer. Pipeline interruptions or rebuilds caused by dependency issues increase both compute cost and engineering effort, leading to inefficiency even when direct spend isn’t immediately visible.
