At Chainbase, I treat 'data contracts' as a first principle to implement. Instead of writing queries first, we first establish contracts for events, dimensions, metrics, and update frequencies, allowing anyone to pull data within those boundaries. This way, the business side discusses the meaning and granularity of the fact tables, while the engineering side discusses latency and cost curves, cutting off disputes at the semantic level. True efficiency comes from a unified language: the same table, the same set of definitions, the same recalculation window, ultimately leading to the same report.
Making governance robust is not a heavy burden. My approach at Chainbase is to provide each core table with a 'health check card': success rate, tail latency percentiles, consistency after cross-chain reorganization, cache hits and misses, and upper limits on single query costs. Any anomalies are indicated on the dashboard, and the on-duty team can simply check each item against the card for troubleshooting. Furthermore, we abstract high-frequency similar queries into templates, using parameterized interfaces to support multi-scenario reuse, which reduces latency and controls costs.
The most easily overlooked aspect is the 'path home.' When data metrics are distorted due to upstream changes, Chainbase's remedy is not to hastily recalculate, but to revert to a certain point in time according to the contract, using a fixed version of the code to uniformly rebuild within a fixed window. After reconstruction, the health check card generates a discrepancy reconciliation, recording when distortion occurred, when it was corrected, and the scope of the impact. From then on, data is no longer just a screenshot or individual memory, but an auditable asset for the organization.