Testing

Data Warehouse Testing

Ensure Quality and Reliability at Every Level

Comprehensive testing services that validate data accuracy, pipeline reliability, and warehouse performance. Build trust in your data with rigorous quality assurance.

80+
Test Engagements
99.9%
Defect Detection
85%
Automation Rate
99.5%
Quality Score
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What is Data Warehouse Testing?

Ensuring trust and reliability in your data

Data warehouse testing is a comprehensive quality assurance discipline that validates every aspect of your data warehouse: the accuracy of data, the reliability of ETL pipelines, the performance of queries, and the integrity of the overall system.

Unlike traditional application testing, data warehouse testing must validate data at scale - billions of rows across hundreds of tables - while ensuring business rules are correctly applied and data relationships are maintained. This requires specialized tools, techniques, and expertise.

Our testing services cover the full spectrum: source-to-target data validation, ETL pipeline testing, business rule verification, performance and load testing, and regression testing for ongoing changes. We implement test automation frameworks that provide continuous quality assurance as your warehouse evolves.

Key Metrics

99.9%
Defect Detection
Pre-production defect catch
85%+
Test Automation
Automated test coverage
99.5%
Data Quality
Validation pass rate
70% faster
Cycle Time
With automation

Why Choose DevSimplex for DWH Testing?

Specialized data testing expertise and automation

Data warehouse testing requires specialized skills that differ from traditional software testing. Our QA engineers understand dimensional modeling, ETL patterns, and the unique challenges of validating data at enterprise scale.

We've developed testing frameworks specifically for data warehouses that enable comprehensive validation without the manual effort that makes data testing prohibitively expensive. Our automation approaches achieve 85%+ automation rates while maintaining thorough coverage.

Beyond finding defects, we help you build quality into your processes. We implement testing frameworks, establish quality gates, and train your team on data testing best practices. The result is a sustainable quality program, not just a one-time test effort.

Requirements

What you need to get started

Test Requirements

required

Definition of what needs to be tested and acceptance criteria.

Data Access

required

Access to source systems and data warehouse environments.

Business Rules Documentation

required

Documentation of transformation logic and business rules.

Historical Test Data

recommended

Known good data sets for validation and regression testing.

Common Challenges We Solve

Problems we help you avoid

Scale of Testing

Impact: Impossible to manually test billions of rows.
Our Solution: Automated testing frameworks with intelligent sampling and validation.

Complex Transformations

Impact: Business rules difficult to validate systematically.
Our Solution: Rule-based test generation and comprehensive edge case coverage.

Regression Risk

Impact: Changes break existing functionality undetected.
Our Solution: Automated regression test suites with continuous execution.

Your Dedicated Team

Who you'll be working with

QA Lead

Leads testing strategy and team coordination.

10+ years data testing

Data Test Engineer

Develops and executes data validation tests.

7+ years DWH testing

Test Automation Engineer

Builds automated test frameworks and pipelines.

5+ years test automation

Performance Test Engineer

Conducts performance and load testing.

5+ years performance testing

How We Work Together

Dedicated testing team integrated with development workflow.

Technology Stack

Modern tools and frameworks we use

Great Expectations

Data validation framework

dbt Tests

Built-in data tests

Python

Test automation

SQL

Data validation queries

Apache JMeter

Performance testing

Testing Investment ROI

Quality assurance prevents costly production defects and builds data trust.

90% reduction
Production Defects
Post-implementation
100%
Data Trust
Validated accuracy
70% faster
Testing Efficiency
With automation

Why We're Different

How we compare to alternatives

AspectOur ApproachTypical AlternativeYour Advantage
MethodologyData-specific testing frameworksGeneric software testingAppropriate techniques for data validation
ScaleAutomated validation at billion-row scaleManual samplingComprehensive coverage without manual effort
SustainabilityReusable automation frameworkOne-time test effortOngoing quality assurance

Our Process

A proven approach that delivers results consistently.

1

Test Planning

1-2 weeks

Analyze requirements, develop test strategy, and define test scope and coverage.

Test strategyTest planTest case inventoryCoverage matrix
2

Test Design

2-3 weeks

Design test cases, define expected results, and prepare test data.

Test casesTest dataExpected resultsValidation rules
3

Automation Development

2-4 weeks

Build automated test frameworks and implement test scripts.

Test frameworkAutomated testsCI/CD integrationTest documentation
4

Test Execution

2-4 weeks

Execute tests, validate results, and report defects.

Test resultsDefect reportsQuality metricsReconciliation reports
5

Regression & Sign-off

1-2 weeks

Execute regression tests, validate fixes, and obtain quality sign-off.

Regression resultsQuality sign-offTest summary reportAutomation handover

Frequently Asked Questions

What types of data warehouse testing do you provide?

We provide comprehensive testing including data quality testing, ETL pipeline testing, source-to-target validation, business rule verification, performance and load testing, integration testing, and regression testing. We tailor the testing scope based on your specific requirements and risk areas.

How do you test data at scale?

We use automated testing frameworks that enable validation at scale without manual effort. Techniques include statistical sampling, automated reconciliation queries, data profiling, and rule-based validation. Tools like Great Expectations and dbt tests enable continuous data quality monitoring.

Can you integrate testing into our CI/CD pipeline?

Yes, we design test automation specifically for CI/CD integration. Tests are triggered automatically with each deployment, providing immediate feedback on data quality. This includes unit tests for transformations, integration tests for pipelines, and regression tests for existing functionality.

How long does a testing engagement typically take?

Testing engagements typically run 6-12 weeks depending on scope. Initial testing for a new warehouse may take 8-12 weeks, while regression testing or specific validation efforts may be completed in 4-6 weeks. We also offer ongoing testing services.

What deliverables do you provide?

Deliverables include test plans, test cases, automated test suites, test results, defect reports, quality metrics, reconciliation reports, and test summary documentation. For automation engagements, we deliver reusable test frameworks with documentation and training.

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Let's discuss how we can help transform your business with data warehouse testing.

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