Integrations
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Automated Understanding Of Your Data Ecosystem
Connect to your source systems, scan schemas, sample data, and build a unified metadata layer
Schema Profiling
Crawls your source systems to extract tables, columns, and relationships
Semantic Inference
LLM interprets column names, descriptions, and values to infer business meaning
PII & Compliance Tagging
Detects personally identifiable or regulated data fields using semantic + regex hybrid detection
Data Quality Signals
Detects anomalies, null ratios, and schema inconsistencies during profiling
Start Migration With Clarity.
Not Chaos
Connect to your source systems, scan schemas, sample data, and build a unified metadata layer
Schema Docs
Manual, Incomplete
Auto-generated
Mapping prep time
Weeks
Hours
PII Coverage
Partial
100% Detected
Downstream Errors
High
Reduced by 40%
Metrics:
Schema Documentation
Mapping Prep Time
PI Coverage
Downstream Coverage
Traditional:
Manual, incomplete
Weeks
Partial
High
Datachecks:
Auto-generated
Hours
100% Detected
Reduced by 40%
How It Works
End-to-End QA, Without the Manual Overhead
Connect
Plug into your source and target systems—whether databases, data lakes, or custom queries.
Plug & Play
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Extract
Automatically capture schemas, lineage, filters, and business rules used in the migration.
Auto Discovery
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Extract
Automatically capture schemas, lineage, filters, and business rules used in the migration.
Auto Discovery
Generate & Run
Let the agent generate test cases, validations, and synthetic data—all tailored to your migration context.
Smart Validation
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Report & Reconcile
View granular results across PKs, rows, and columns. Drill down to mismatches, export audit-ready reports, and close the loop faster.
Audit-Ready
Ideal For
Built for Teams Driving Complex Data Change
Validate every migration milestone, reduce QA effort, and deliver data your business can trust.
Roles
Data Engineering Teams, System Integrators, Migration PMOs, Internal Audit / Data Governance
Projects
Snowflake, BigQuery, Databricks migrations, Mainframe or Oracle legacy shutdown, Large-scale ERP/CRM modernization, Data lake replatforming
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Efficiency Boost
Clients report a 90% reduction in manual QA effort and up to 3x faster migration validation cycles after switching to Datachecks.
Datachecks has undergone SOC 2 Type II attestation and undergoes annual audits.
Infrastructure that meets ISO standards.
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Deployed to your infrastructure, so no data leaves your network.
Every user action is audit trailed.
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Your questions, our answer
How does the Discovery Agent analyze legacy databases without disrupting them?
It connects via read-only credentials to capture schema metadata and sample data safely — no writes, no performance impact, and no production interference.
Does it identify hidden or undocumented fields?
Yes. The Discovery Agent detects unreferenced or unmapped fields and automatically infers business meaning, dependencies, and sensitivity — even when documentation is missing.
How accurate is its semantic inference for column meaning?
The model is fine-tuned on millions of enterprise data patterns and achieves >90% accuracy in inferring business context like “cust_id → Customer Identifier.” Engineers can review and adjust interpretations easily.
How do teams use Discovery results downstream?
Discovery outputs feed directly into the Translation and Validation Agents, creating an intelligent metadata foundation for automated code rewrites and QA reconciliation.
What kind of ROI can we expect?
Most teams reduce discovery and documentation time by 70% or more, cutting weeks of manual profiling into a few hours — and preventing costly downstream migration errors.
How does Datachecks handle schema or logic differences between systems?
The platform detects incompatible data types, function mismatches, and structural variations automatically — and recommends equivalent mappings or rewrite rules to ensure consistent behavior in the target platform.
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