Data Quality Validator
Data quality auditing workflow built with Langflow that validates datasets against strict rules and routes records into clean vs quarantine pipelines with structured outputs.
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This Langflow flow helps teams trust their analytics by catching data issues before they propagate. It audits datasets against strict business rules—syntax, logic, and decimal precision—and automatically routes records into clean or quarantine pipelines with clear reasons. With a customized Batch Run component that injects temporal context and parses complex JSON into structured columns, the workflow produces audit-ready outputs that can be analyzed immediately and used to drive fast remediation.
How it works
This Langflow flow implements an automated data quality auditing and routing pipeline.
It starts by ingesting a dataset (e.g., CSV or JSON) and a set of validation rules such as schemas, required fields, allowed value ranges, cross-field consistency checks, and decimal precision constraints. A temporal context input (as-of date/time) is injected during batch processing so validations can be evaluated consistently for time-sensitive logic.
A customized Batch Run component processes records in a deterministic, repeatable way and calls an AI-driven auditor to evaluate each record against the rule set. The auditor returns detailed results, including pass/fail status, error categories, and remediation guidance.
The flow then auto-parses complex JSON audit outputs into structured columns so downstream analysis is immediate (e.g., error_type, field_name, severity, rule_id, suggested_fix). Records are routed into two pipelines: clean (ready for downstream systems) and quarantine (flagged for review or remediation).
Finally, summary reporting components generate aggregate metrics (failure rates by rule, top error types, affected sources) so teams can prioritize fixes and improve upstream data collection.
Example use cases
• Data teams can validate ingestion feeds nightly and quarantine malformed or inconsistent rows before they reach dashboards or warehouses.
• Finance teams can enforce decimal precision and reconciliation logic on transaction datasets to prevent reporting errors and audit issues.
• Operations teams can catch invalid routing fields, missing IDs, or impossible timestamps in event streams and isolate bad records automatically.
• Compliance teams can validate required fields and formatting rules for regulated datasets, producing structured audit trails for governance.
• Analytics engineers can monitor data quality regressions by tracking failure rates and top rule violations over time.
The flow can be extended into a full data quality program. Add connectors to warehouses and ELT tools to validate upstream tables on a schedule, and push quarantine outputs to a remediation queue (Jira/Slack) with owners and SLAs. Store audit history in a database for trend analysis and regression detection. You can also implement source-level routing (quarantine only specific producers), severity-based escalation, and automated backfills after fixes. Advanced setups can generate data contracts from the rule set, publish quality SLAs, and integrate with observability dashboards to track quality KPIs alongside pipeline health.
What you'll do
1.
Run the workflow to process your data
2.
See how data flows through each node
3.
Review and validate the results
What you'll learn
• How to build AI workflows with Langflow
• How to process and analyze data
• How to integrate with external services
Why it matters
Data quality auditing workflow built with Langflow that validates datasets against strict rules and routes records into clean vs quarantine pipelines with structured outputs.
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