CSV/JSON export or transaction sample.
Technical demonstration
Synthetic data · independent implementation · repeatable quality rules
Data quality control in a pipeline
ingestion · transformations · quality rules · lineage · regression
Buyer outcome: data quality report, rejected records, source-to-target transformation trace and comparison between runs.
First scope: data sample, export or pipeline fragment. Responsibility covers agreed transformations, quality rules, regression scenarios and acceptance report.
Ingestion → staging → normalization → transformation.
Schema, type, reference, transformation and consistency.
Quality report, quarantine, lineage and run differences.
Contract, rules and transformations produce the expected result.
Pipeline fragment acceptance package
From raw value to accepted dataset
The target dataset is visible before acceptance. A faulty record goes to quarantine and does not increase the accepted dataset. Expected REJECT in a negative scenario gives the test a PASS result.
REFERENCE CONFIGURATION
Reference transformations contain no intentional change. The current input is checked independently; PASS is not assumed upfront. The reference package establishes RUN A for run comparison.
For a technical reviewer
Inspect input, rules, run identifier and evidence in the tabs below.
What you can verify here
Data contract, transformations, quality rules, error source, field-level lineage, expected/actual, difference between runs and repeatable acceptance report.
Acceptance criteria
Reference: all scenarios PASS. Controlled TX-104 transformation: one root discrepancy, one changed record and one field. A rejected record does not enter the accepted target dataset. Reset: PASS again and a clean comparison.
Demonstration scope
Controlled pipeline fragment: ingestion, normalization, transformations, quality rules and source-to-target trace. Adapter work for a Data Lake/ETL environment is a separate integration scope. Data and customer dimensions are synthetic.
Public dataset: CSV/JSON, up to 1000 records for the repeatable test scenario. One root rejection cause per record; validation stops the record at the first blocking rule. Field lineage covers records that reached transformation. Fixed clock: September 16, 2026. The backend isolates reports by browser session and does not write to source systems.
Describe the scope