QuerySurge + MongoDB

QuerySurge delivers continuous, automated
validation for MongoDB - so every decision is
powered by trusted data.

Why QuerySurge + MongoDB

MongoDB powers modern apps with flexible document storage and rich aggregation pipelines. That flexibility can also mask schema drift, null explosions, nested array issues, and transformation errors.

QuerySurge closes the gap with purpose-built, automated tests for semi-structured data, catching defects early and proving data integrity end-to-end.

How It Works

Direct Connectivity

Connect QuerySurge to MongoDB / MongoDB Atlas using:
- MongoDB BI/SQL interface (JDBC) for SQL-style validation, or
- Native/connector workflows that export JSON/Parquet views for comparison.
- Test collections, views, aggregation outputs, and curated analytic datasets.

End-to-End Data Validation

Compare source → MongoDB collections → downstream targets (warehouses/BI).
Validate aggregation pipeline results, calculated fields, and denormalized shapes.
Handle nested documents & arrays with configurable flattening/expansion.
Tolerance rules for %/count-based exceptions to reduce noise.

DataOps & CI/CD

Trigger QuerySurge tests from Atlas Triggers/Functions, Airflow, dbt, Jenkins, GitHub Actions, and more.
Enforce quality gates in every build/release to stop bad data before production.

Reporting & Audit

Pass/fail dashboards with row, cell, and field-level detail.
Column/field failure analytics to pinpoint problematic keys quickly.
Audit-ready history of every run for governance and compliance.

Key Benefits

Benefit	Why It Matters
Trust in Semi-Structured Data	Validate JSON/BSON fields, arrays, and nested objects with confidence
Catch Drift Early	Detect schema drift, missing keys, type changes, and null spikes
Pipeline Assurance	Prove aggregation logic and transformations are correct
Scale with Your Apps	Validate millions of docs and large result sets efficiently
Compliance-Ready	Produce evidence of testing for auditors and stakeholders

Ideal Use Cases

Operational to Analytics: Validate extracts from MongoDB into warehouses (i.e. Snowflake/Redshift/ BigQuery) or data lakes.
Microservices & Event-Driven: Confirm event payloads and derived projections are complete and consistent.
Migration & Modernization: Verify moves to MongoDB Atlas or refactoring schemas. BI & Reporting: Ensure KPIs in dashboards exactly match the MongoDB source truth.

Implementation Snapshot

Connect to MongoDB/Atlas collection(s) or BI interface.
Configure QueryPairs to compare source ↔ aggregation output orMongoDB ↔ data warehouse/BI.
Set tolerances, schedule runs, and wire into CI/CD.
Monitor dashboards, drill into mismatches, and export audit reports.