Data quality monitoring requires engineers to manually check data arrival, run validation queries, and compare results against thresholds multiple times daily. This scattered, repetitive work creates delays in detecting quality issues and consumes significant engineering capacity.
Automation monitors data arrival continuously, executes all validation rules automatically, and alerts the team to issues within minutes. Approved data flows directly to the warehouse with a complete audit trail.
The full workflow, from trigger to completion.
A new data file lands in the source system or an API call is received. The automation platform detects this event and initiates the pipeline.
The automation extracts raw data from the source and stages it. File format and basic schema checks are performed automatically.
A validation executes all predefined quality checks: row counts, null percentages, date ranges, duplicate keys, and business rule thresholds. Results are logged.
The automation calculates an overall quality score based on the number of rules passed. If the score meets the threshold, data is approved for load.
A summary of validation results, including any failures, is posted to a dedicated channel. The team can review and take action in real time.
If approved, the validated data is automatically loaded production schema. If rejected, data is quarantined and flagged for manual review.
Validation metadata, timestamps, rule results, and load status are automatically written to a Google Sheet for audit and compliance tracking.
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