Consider an example that models two steps of a flow: charge creation

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Potential money movement) and release (funds becoming available). As you follow the flow, keep in mind these definitions: Accounts are buckets of money distinguished by their type (e.g., charge_unsubmitted) and properties (e.g., id, business). Events move money between accounts (e.g., charge.creation and charge.release Completeness We guarantee data completeness and guard against missing data from upstream systems with explicit cross-system checks alongside automated anomaly detection. For example, we ensure that every ID in a producer database has a matching Ledger event. We also run statistical modeling on data availability. We have models for every account type that use historical trends to calculate expected data arrival time and, if events do not appear, we interpret this as potentially missing data. .

But, if the release event is never published or has the wrong id, Ledger would not clear the balance in the associated charge_undisbursed account, and it would instead hold the balance in a different instance of charge_undisbursed. Example clearing issue Consider next how a wrong value (business: B vs. business: A) results in two clearing accounts with nonzero balance. Instead of having one reservoir of money for business Brazil Mobile Number List we wind up with two—one for business: A and one for business: B. Blog > Ledger > T missing event Generalizing from this example, we repeat this process for every fund flow, account type, and property-based subdivision inside of Ledger. How teams at Stripe explore DQ metrics On top of the DQ Platform, we built hierarchical automated alerting and rich tooling. We combine interactive metric displays with analysis and guidance. The experience for internal leaders and team members focuses on proactive feedback, simple manipulation of data, and meaningful metrics.



Even when we have billions of transactions, a single missing, late, or incorrect transaction immediately creates a detectable accuracy issue with a simple query—for example, “Find the clearing Accounts with nonzero balance.” Timeliness Clearing prevents persistent problems, but we also need to guarantee data arrives on time for time-sensitive functions such as monthly report generation. Producers create time stamps when integrating with Ledger, and we measure the delta between when data first enters the Stripe platform and when it reaches Ledger. We set a hard threshold on the data delivery window, and we create headroom for subsequent reporting, analysis, and manipulations to guarantee % timeliness.