Data integrity and ETL pipeline validation to ensure your data flows accurately, transforms correctly, and lands reliably in every environment.
From raw source data to final target state — we validate every transformation, constraint, and reconciliation step in your data pipeline.
We validate every extract, transform, and load step in your pipeline — verifying data moves correctly from source systems to target databases without loss or corruption.
Row counts, null value checks, duplicate detection, and referential integrity validation — ensuring your data is complete, consistent, and trustworthy at every stage.
We verify that database schemas, primary keys, foreign keys, unique constraints, and data type definitions are correctly enforced across all environments.
Business transformation rules — aggregations, calculations, lookups, and mappings — are tested systematically to confirm the logic produces the expected output.
Database migrations are validated before go-live — verifying that all records are correctly migrated, no data is lost, and rollback procedures work as expected.
We profile slow queries, missing indexes, and table scans — identifying performance bottlenecks in your database layer before they impact application response times.
We treat data quality as a first-class concern — validating not just application behaviour, but the accuracy and completeness of the data powering it.
Our team has hands-on experience with ETL tools like dbt, Apache Spark, Airflow, and custom SQL pipelines — testing at every layer of the data stack.
We systematically verify that your database schema matches its specification — catching missing columns, wrong data types, and broken constraints early.
We validate every database migration with before-and-after comparisons and rollback testing — so you can deploy schema changes with full confidence.
We study your data mapping documents, transformation rules, and pipeline design to understand exactly what data should flow where and how it should look at each stage.
We prepare representative test datasets — including edge cases like nulls, boundary values, and large volumes — to ensure comprehensive pipeline coverage.
The pipeline is executed and we validate outputs at every stage — verifying row counts, transformation results, and target data against source-to-target specifications.
Source and target datasets are reconciled record by record — identifying discrepancies, data loss, duplication, or transformation errors that need to be addressed.
A complete data quality report is delivered covering all findings, reconciliation summaries, defect logs, and prioritized recommendations for your data engineering team.
Get a free data quality consultation. We'll assess your ETL pipeline and database setup and identify your highest-risk data quality gaps.
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