Recon Engine
Data Reconciliation & Validation
Reconcile Your Data with Complete Confidence
Problem
Data flows through multiple systems, where records can be lost, duplicated, or corrupted at each step. Teams perform reconciliation manually, making it slow, error-prone, and incomplete. Financial and regulatory data must match exactly across systems.
Purpose
Features
Connectors
Link databases, warehouses, and files across systems.
Flexible Recon
Run on demand, scheduled, or event-triggered.
Quarantine Reports
Isolate discrepancies with drill-down and exports.
Tolerance Rules
Set variance thresholds for financial and operational data.
Aquila Integration
Auto-diagnose and fix reconciliation failures.
Contract Integration
Validate results against SLA commitments.
Impact
How Recon Engine Works
Use Cases
The Challenge
A nightly ETL pipeline transfers 50M+ records from Oracle to Databricks. Occasionally, silent failures occur, resulting in missing or duplicated records that remain undetected for days, impacting downstream analytics and reporting.
Solution:
With Eagle Eye IQ’s automated reconciliation capabilities, post-load validation runs immediately after pipeline execution:
• Row counts between source and target systems
• Checksum integrity for data validation
• Aggregate metrics such as SUM, AVG, and COUNT across key columns
Any discrepancies are instantly flagged, and engineers can drill down into mismatched records for faster investigation and resolution.
Eagle Eye IQ ensures 100% transfer integrity verification within minutes, eliminating the risk of silent data loss or duplication in critical pipelines.