Test & Certify Data Migrations with iceDQ

Database migrations look straightforward until a decimal precision difference corrupts financial records; an encoding mismatch breaks a downstream application, or a silent data type conversion truncates years of historical data. Database migration data testing validates structural accuracy, data completeness, and application compatibility between source and target before cutover, not after.

• Schema and data type testing: Compares source and target schemas at the column and data type level before migration begins, identifying incompatibilities including decimal precision differences, encoding mismatches, and date format changes that cause load failures or silent data corruption in the target.
• Full volume data reconciliation: Validates record counts, aggregated values, and row-by-row column-level data between source and target databases across every table, ensuring no records are lost, truncated, or corrupted.
• Transformation integrity testing: Detects encoding mismatches, decimal precision differences, and date format inconsistencies at the column level that row count and aggregation checks do not surface, preventing silent data corruption from reaching downstream applications.
• Application dependency testing: Validates that data consumed by downstream applications, APIs, and reports continue to conform to expected formats, ranges, and referential integrity constraints after migration to the target database.
• Stored procedure and view testing: Validates that database objects including stored procedures, views, and functions produce identical outputs in the target database, ensuring business logic embedded in the database layer is not broken by the migration.

Migrating enterprise data warehouses to modern cloud platforms introduces risk across historical data, dimensional models, aggregations, and downstream reporting. iceDQ certifies warehouse completeness, reconciliation accuracy, and analytics consistency to ensure trusted business reporting after migration.

• Schema and warehouse structure integrity: Validate tables, columns, datatypes, dimensional models, and warehouse structures across source and target environments.
• Historical and incremental load assurance: Validate historical migrations, CDC loads, and incremental refreshes independently to prevent compounding data inconsistencies.
• Aggregate and analytical consistency: Validate warehouse aggregates, summarized datasets, and analytical outputs to ensure consistent downstream analytics after migration.
• Cross-Layer data reconciliation: Reconcile staging, curated, semantic, and reporting layers to detect missing, duplicated, or transformed data discrepancies across the warehouse ecosystem.
• Warehouse data completeness assurance: Compare large-scale warehouse datasets across source and target platforms to certify migration completeness and analytical accuracy.