What is Data Reconciliation?

The Core Problem: In distributed systems, the same business entity lives in multiple places. A payment record exists in your payment processor, your ledger service, your data warehouse, and your machine learning feature store. Once data leaves the primary system of record, you can no longer rely on ACID (Atomicity, Consistency, Isolation, Durability) transactions to keep these views synchronized. This creates a trust problem. Without reconciliation, you have no systematic way to verify that all these copies are actually telling the same story. The Three Pillars: Every reconciliation system addresses three core challenges. First is matching, which is deciding that a record in system A corresponds to a record in system B. You might use a primary key like order_id, a composite business key like (user_id, transaction_date), or more advanced fuzzy matching. Second is comparison, which is deciding what to check once records are matched. Lightweight approaches compare just counts and aggregate checksums. Thorough approaches compare every field value, business rules, and derived metrics. Third is resolution, which is what you do when differences are found. This ranges from automatic correction (like rerunning an ETL job) to raising critical incidents when money or compliance is involved.