Data Reconciliation
Data Reconciliation identifies inconsistencies and critical changes between datasets by comparing row-by-row table data, across sources — without writing Python scripts or complex coding.
Ensure Data Integrity with Data Reconciliation Checks
Critical for ensuring high-quality data, reconciling data involves comparing row-level values from different tables to check the accuracy, consistency, and completeness of data.
Data breaks, all the time. Sometimes for no reason. And when data is in motion — moving from different source systems to various target destinations — data is especially prone to errors or mutations.
By using Lightup for Data Reconciliation, enterprise data teams can easily find data discrepancies, so they can be resolved quickly — before downstream escalation.
Key Use Cases for Reconciling Data
Cloud Migration
For Cloud migrations, uncertainties arise when handling unfamiliar data, its life cycle, and the entire data management process.
Lightup’s Data Reconciliation Checks show disparities between source and target systems, increasing user trust and accelerating Cloud migrations.
Data Pipeline Validation
Whenever data moves or is transformed, every handover increases the potential for data mutation or silent changes.
Engineering teams reconcile data to ensure a robust data pipeline validation process during data migrations.
Lightup Data Reconciliation Checks
Also referred to as Data Compare or “Data Diffing,” Lightup’s Data Reconciliation Checks enable you to compare data row by row from different sources to see if any values have changed during migrations or data pipeline validation processes.
- Comparing Key and Target Columns
In Lightup, select key and target columns to uniquely identify records and focus on relevant data points for comparison.
- Optimized Data Extraction for Comparison
Lightup extracts the appropriate data, optimizing the data reconciliation process by minimizing data volume.
- Local Data Comparisons
Advanced algorithms analyze data locally, identifying discrepancies without requiring data transfers between sources.
- Discarding Raw Data
Lightup discards raw data post-comparison, mitigating potential security and compliance risks.
- Percentage Matching
See the percentage of matching data for quick identification and resolution of discrepancies.
Why Lightup for Data Reconciliation?
Deliver trusted data for business-critical applications and operations, mitigating risks of costly errors and downstream escalations.
Efficiency
Streamline data reconciliation processes by automating data comparison tasks, saving time and resources.
Accuracy
Ensure data accuracy by comparing data row by row, across different data sources, increasing user adoption and confidence.
Security
Discard raw data post-comparison, protecting sensitive information from unauthorized access.
Ease-of-Use
Lightup’s user-friendly interface makes it easy to interpret results and take action.
Improved Decision-Making
Improve data reliability by reconciling data, leading to better decision-making and trusted insights.
Customization
Customize the reconciliation process to specific business needs with Lightup’s flexible no-code, low-code, and custom SQL queries.
Resources
See how Data Reconciliation works in Lightup,
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