You oversee a BigQuery data pipeline for an analytics platform, with daily data loads and transformations via an ETL pipeline. This pipeline undergoes frequent updates, sometimes introducing errors that may go unnoticed for up to two weeks. Your goal is to implement a strategy that allows for error recovery while optimizing backup storage costs. What is the best approach?