QuerySurge & dbt
Integrated to Deliver Trusted,
Analytics-Ready Data
Dbt (data build tool) and QuerySurge serve very different but highly complementary roles in the modern data stack. Here’s how they can work together effectively.
Roles in the Data Lifecycle
-
dbt
- Focuses on data transformation in the data warehouse/lakehouse.
- Helps teams define transformations as version-controlled SQL models.
- Handles lineage, documentation, and testing (but primarily lightweight unit or schema tests).
- Designed for analytics engineers and data engineers building reusable, modular data pipelines.
-
QuerySurge
- Specializes in data validation and testing across the pipeline.
- Validates data from source systems through ETL/ELT transformations into warehouses and BI reports.
- Provides full regression testing, automated test execution, dashboards, CI/CD integration, and audit trails.
- Designed for QA teams, data engineers, and compliance/governance stakeholders who need assurance of accuracy and readiness-for-analytics.
How They Complement Each Other
Pre-Validation vs. Post-Validation
- dbt builds and transforms the warehouse models.
- QuerySurge validates that what dbt built is accurate, complete, and matches source data and business rules.
Lightweight vs. Deep Testing
- dbt offers simple schema checks (“no nulls,” “unique keys,” “referential integrity”).
- QuerySurge goes deeper: full row/column-level comparisons between source and target, transformation verification, cross-system reconciliation, and BI report-to-database validation.
DevOps/DataOps Alignment
- dbt is developer-centric with GitOps-style workflows.
- QuerySurge plugs into CI/CD pipelines as a data quality gatekeeper, ensuring only validated data flows into production or reporting layers.
Governance & Auditability
- dbt documents transformations for transparency.
- QuerySurge provides compliance-grade audit trails, dashboards, and analytics for regulatory or governance requirements.
Side-by-Side Comparison of Synergy
Category |
dbt |
QuerySurge |
Synergy |
|---|---|---|---|
Primary Focus |
SQL-based data transformation in data warehouse / data lakehouse |
Automated data validation & testing across pipelines, sources, targets, and BI reports |
dbt builds trusted models, QuerySurge proves their accuracy |
Deployment |
Open-source or dbt Cloud SaaS. GitOps style workflows |
On-prem or cloud; integrates with CI/CD pipelines |
dbt builds; QuerySurge validates within same DevOps cycle |
Target Users |
Analytics engineers, data engineers |
QA engineers, data engineers, compliance teams |
Shared ownership of data quality across engineering + QA |
Ease of Use |
SQL-first, developer-friendly, modular models |
AI-powered test creation, no-code/low-code test design, wizard-driven connectors, dashboards |
Engineers build in dbt; QA/business validate in QuerySurge |
Testing |
Lightweight schema & unit tests (null checks, unique keys, constraints) |
Full regression, source-to-target comparisons, transformation verification, BI report-level testing |
dbt provides quick checks; QuerySurge ensures deep accuracy |
Automation |
Git-based workflows, scheduled jobs, CI/CD triggers |
AI-powered test creation, data quality gates, seamless CI/CD integration, analytics dashboards |
Combined: CI/CD → run dbt models → auto-trigger QuerySurge validation |
Data Coverage |
In-warehouse models only (Snowflake, BigQuery, Redshift, Databricks, etc.) |
200+ data connectors: databases, files, APIs, cloud apps, BI platforms |
QuerySurge extends validation beyond warehouse into BI reports & source systems |
Lineage & Governance |
Transformation lineage, documentation, and metadata in dbt docs |
Audit trails, compliance reporting, ready-for-analytics dashboards |
dbt shows what changed; QuerySurge proves it’s correct |
Value Proposition |
Streamlined, version-controlled data transformations |
End-to-end trust in data accuracy and readiness |
Together: They deliver both agility and assurance |
Key Takeaway
- dbt builds trust in the process of transforming data.
- QuerySurge builds trust in the outcome of that data across the full pipeline.
Together, they create an end-to-end ecosystem where data is not only transformedefficiently but also proven to be accurate and analytics-ready.
