Quality Engineering
Database TestingSchema, migration, ETL, and data-integrity validation.
Data bugs are the quietest and most expensive kind. We validate schemas, migrations, integrity constraints, and query performance across SQL and NoSQL systems so releases don't silently corrupt or slow down your data layer.
What this covers
Schema and constraint validation
Migration Testing, forward, rollback, and data-preservation checks
Data integrity and reconciliation Testing across services
Query performance profiling and index review
ETL and reporting-pipeline validation
The layer where bad data becomes permanent
A UI defect shows a wrong number.
A UI defect shows a wrong number. A data defect stores one. The first is embarrassing and reversible; the second quietly corrupts reports, invoices, entitlements and audit trails, often for months before anyone notices, and by then the bad rows have been replicated, backed up and consumed by three downstream systems.
Database Testing verifies that what the application claims to have saved is what is actually stored, that constraints and relationships genuinely hold, that transactions are atomic under concurrency, and that migrations move data without losing or mangling it. It is unglamorous work with an outsized return, particularly in healthcare, utilities and billing and any product where a number ends up on an invoice.
What we verify
We work across PostgreSQL, MySQL, SQL Server, Oracle and MongoDB, using SQL-based validation harnesses, typically in Python, that can be re-run in CI rather than a one-off Manual reconciliation.
Integrity and constraints: primary and foreign keys, uniqueness, nullability, check constraints and cascade behaviour, enforced in the database, not merely assumed by the application.
CRUD correctness: that every write path persists exactly what the API accepted, including precision on decimals, timezone handling on timestamps, and encoding on text.
Transactions and concurrency: atomicity and isolation under parallel load, deadlock behaviour, and whether partial failures roll back cleanly.
Migrations: forward and rollback paths validated against production-shaped volumes, with row counts, checksums and spot reconciliation before and after.
Stored logic: procedures, triggers, views and jobs, which frequently escape application-level Testing entirely.
ETL and reporting: that the warehouse or reporting layer reconciles with the source of record rather than drifting from it.
Test data that resembles reality
Most data defects hide behind sanitised, tidy fixtures.
Most data defects hide behind sanitised, tidy fixtures. We build test data that carries the shapes production actually contains, the customer with 40,000 orders, the record with a NULL nobody expected, the unicode name, the timezone-straddling timestamp, the legacy row written by a version of the schema that no longer exists, while keeping personal data out of non-production environments through masking and synthesis.
Engagement path
How the engagement runs
Every Database Testing engagement runs the same way: understand the risk, build the thing that reduces it, prove it works, hand it over.
Schema and rule review
We read the schema, the constraints and the stored logic, and identify where the application is trusting rules the database does not actually enforce.
Data quality harness
Re-runnable SQL validation for integrity, reconciliation and business rules, plus masked, production-shaped test data to exercise real edge cases.
Transaction and migration Testing
Concurrency and rollback behaviour under parallel load, and migration dry runs at production volume with before-and-after reconciliation.
CI integration
Data quality checks wired into the pipeline so schema drift and rule violations surface on a build rather than in a quarterly report.
Deliverables
What you get
Artefacts you keep and can run without us. Everything lives in your repositories and your tooling.
Handover pack
6 artefacts · yours to keep
A documented map of integrity rules and where they are actually enforced
A re-runnable SQL validation harness, version-controlled in your repository
Masked, production-shaped test data sets safe for non-production use
Migration dry-run results with row counts, checksums and reconciliation
Concurrency and rollback findings with reproduction steps
CI-integrated data quality checks that fail on drift
Self-check
Signs your team needs this
If more than one of these is true, it is usually cheaper to fix now than after the next release.
Reports and the application disagree, and nobody can say which is right
A migration is planned and rollback has never been rehearsed at volume
Constraints live in application code rather than in the schema
Stored procedures, triggers and scheduled jobs are outside any test coverage
Duplicate, orphaned or mis-rounded records keep appearing in production
A Senior Engineer replies within one business day. NDA first.
Questions
Frequently Asked Questions
Straight answers, written the way we'd say them on a call.
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Where to go next
Database Testing rarely stands alone. These are the disciplines and resources teams pair it with most often.
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- Within one business day, every time
- NDA available before you share any details
16+
Years QA leadership
16
Testing disciplines
6
Markets served
1
Business day to reply
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