Solution Data Integrity

Data Validation & Reconciliation.

Continuous validation of schema, counts, uniqueness and business rules across source → target systems, with audit-ready reconciliation and alerts.

Audit-ready · Traceable · NDA available
Best fit
  • Legacy → Cloud migrations
  • ETL / CDC / DWH pipelines
  • Regulated & audited environments
  • Multi-source integrations
Business outcomes
  • • Fewer data defects
  • • Audit-ready evidence
  • • Faster go-live
Constraints handled
  • • Schema drift & transformations
  • • Large datasets
  • • Compliance requirements
What we deliver
  • • Validation ruleset
  • • Reconciliation flows
  • • Reports & alerts
Problem

Data issues are discovered too late: missing records, schema drift, broken aggregations or silent transformations that fail audits.

Solution

A validation engine that continuously compares source vs target and produces traceable reconciliation, alerts and reports.

01
Schema & Type Validation

Validate mappings, datatypes, nullable rules and required fields across systems.

02
Counts & Uniqueness

Compare row counts and detect duplicates after transformations.

03
Business Rules

Validate KPIs, totals and domain-specific logic.

04
Audit & Reporting

Reconciliation logs, error reports and dashboards for governance and audits.

Architecture

Source systems feed ETL/CDC pipelines. Validation runs in parallel and produces reconciliation feedback, alerts and audit trails.

Data Validation & Reconciliation Architecture
Previous
Kafka Self-Healing Integrations
DLQ · retries · recovery workflows
Next
Observability & SLA Operations
SLOs · alerts · RCA dashboards

Need data integrity you can prove?

We tailor validation rules and reconciliation strategy to your data model and compliance needs.

Discuss your scenario