QuerySurge & Parquet Files
Validate Parquet files across data lakes, lakehouses, and downstream analytics
Apache Parquet has become a standard format for analytics and data lake architectures.
Its columnar storage and compression make it ideal for large-scale processing, but those same characteristics can make validation and reconciliation harder to manage.
QuerySurge integrates directly with Parquet files, enabling teams to automate data validation across data lakes, pipelines, and downstream analytics without manual inspection or custom scripts.
Why QuerySurge + Parquet
Parquet is optimized for performance, not for human review.
When Parquet files are used as sources, targets, or handoff points in data pipelines, teams still need to answer basic questions:
- Is the data complete?
- Does it reconcile to upstream systems?
- Did transformations introduce errors?
QuerySurge and Parquet together allow teams to:
- Validate Parquet files against databases, data warehouses, and other file formats
- Detect missing, duplicate, or incorrect data early in the pipeline
- Replace one-off Spark jobs and ad-hoc scripts with reusable validation tests
- Provide auditable proof that Parquet-based datasets are accurate and trustworthy
This is especially important in cloud data lakes, data lakehouse architectures, and migration projects where Parquet is the system of record.
How the Integration Works
Connect to Parquet files
QuerySurge reads Parquet files directly from data lakes, cloud storage, or file systems, treating them as structured, testable datasets.
Define validation logic
Using SQL-based tests or Query Wizard, teams define rules to compare Parquet data to source systems, target warehouses, or other Parquet files.
Automate execution
Tests can run on demand, on schedules, or as part of CI/CD and orchestration workflows when new Parquet files are produced.
Analyze results and exceptions
QuerySurge identifies row-level and aggregate mismatches, schema issues, and rule violations with clear pass/fail outcomes.
Report and audit
Results are stored with full history, enabling trend analysis, root-cause investigation, and audit-ready documentation.
Key Benefits
Benefit |
Why It Matters |
|---|---|
Trust Data Lake Assets |
Ensure Parquet files used for analytics and AI are accurate and complete. |
Validate at Scale |
Handle very large Parquet datasets without manual sampling or custom code. |
Shift left in the Pipeline |
Catch data issues as Parquet files are created, before they impact BI, ML, or reporting. |
Reduce Engineering Overhead |
Eliminate fragile validation scripts and Spark jobs with reusable, centralized tests. |
Support Modern Architectures |
Ideal for cloud data lakes, lakehouse platforms, and distributed analytics environments. |
Audit-Ready Reporting |
Dashboards, drill‑downs, and exportable reports for SOX, HIPAA, GDPR, and internal governance. |
Common use cases
- Data lake ingestion validation
- Lakehouse and warehouse reconciliation
- Cloud migration and modernization projects
- Downstream BI and analytics validation
- AI and machine learning data quality checks
Bottom line
QuerySurge makes Parquet files a first-class citizen in your data validation strategy, giving teams confidence that high-performance, columnar data is also correct, complete, and ready for analytics.
