Data Reconciliation ensures that data remains consistent as it moves across systems and layers. It also verifies the alignment of financial or other business‑critical data across systems.
iceDQ automatically reconciles complex data across databases, files, APIs, SAP, cloud platforms, and streaming pipelines. It generates exception reports at the column level and maintains a thorough audit trail for each execution.
Data Reconciliation for Data Pipeline Testing
- Test ETL Process: Test if the ETL pipeline has extracted, transformed and loaded data based on requirement specifications. Verify millions of records at scale ensuring complete test coverage.
- Data Migration Testing: Certify your data migration, schema, initial load, incremental loads between on-premise and cloud across different vendor platforms.
- Row Level Data Transformations Testing: Verify complex transformations such as calculations, aggregations, and conversions.
- Checksum Reconciliation: Reconcile aggregate sums, counts, average, min, and max values across sources with segmentations.
Data Reconciliation for Data Pipeline Testing
- Test ETL Process: Test if the ETL pipeline has extracted, transformed and loaded data based on requirement specifications. Verify millions of records at scale ensuring complete test coverage.
- Data Migration Testing: Certify your data migration, schema, initial load, incremental loads between on-premise and cloud across different vendor platforms.
- Row Level Data Transformations Testing: Verify complex transformations such as calculations, aggregations, and conversions.
- Checksum Reconciliation: Reconcile aggregate sums, counts, average, min, and max values across sources with segmentations.
BI Report Reconciliation Testing
- Database vs. Semantic Model Reconciliation: Ensure that KPIs, relationships, and calculated fields in semantic models accurately represent the source database.
- Semantic Model vs. Reporting: Reconcile the data displayed in dashboards, visualizations, and KPIs with the semantic model to ensure no discrepancies.
- BI Report vs Data Source: Reconcile report or sub‑report data against the database to confirm all values match.
- BI Report vs Report Reconciliation Testing: Compare the underlying data across reports on the same or different BI platforms such as Power BI vs. Tableau or Power BI vs. Power BI across environments
Data Layer Reconciliation
- Source(s) vs. Staging Layer: Reconcile ERP, CRM, and operational data with APIs against the staging layer to identify loss, duplication, or corruption.
- Staging Layer vs. Data Warehouse / Data Lake: Confirm that transformations, cleansing rules, and business logic are applied correctly.
- Medallion Layer Reconciliation: Validate data consistency across bronze, silver, and gold layers by reconciling records at each stage of the pipeline.
Business Data Reconciliation
- Banking Reconciliation: Reconcile bank transactions, customer payments, credit card activity, and financial amounts across internal and external systems.
- Custodian Stock Reconciliation: Validate positions, balances, and transaction‑level data between custodian reports and internal books and records.
- Intercompany Data Reconciliation: Reconcile inventory balances, orders vs. shipments, discounts, and other intercompany transactions across ERP, CRM, and operational systems.
- Insurance Reconciliation: Validate Bordereau reports, premium and claims amounts, and written vs. paid values across policy, claims, and financial systems.
- Compliance Reconciliation: Automate regulatory reconciliations such as FINRA, BCBS‑239, and other compliance‑driven controls.
PRODUCT HIGHLIGHTS
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Performance & Scalability: |  |
Reconcile millions of records in minutes, not hours, with iceDQ's in-memory rules engine and no restriction on the number of records or columns that can be processed. |
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Row-Column Level Reconciliation: |  |
iceDQ matches data at row and column level identifying defects across platforms at the most granular level. |
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Aggregate Reconciliation: |  |
Validate sum, count, average, min, and max values across source and target at any business dimension such as region, product, or date to catch value drift without running a full row level comparison. |
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200+ Cross Platform Reconciliation: | |
iceDQ natively supports reconciliation across files, APIs, databases, applications, BI reports, SAP, CRM, cloud vs on-premises, Kafka and many more. |
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Virtual Dataset Reconciliation: | |
Combine data from multiple sources into a virtual dataset and reconcile against a third target system. |
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Out Of Box Reconciliation: | |
Built-in templates for various reconciliation types such as aggregate, financial recon, referential, source to target, incremental loads, and schema with parameter support. |
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Granular Exception Report: | |
Every reconciliation run produces an exception report with mismatches at record and column level showing source value and target value for each discrepancy. |
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Fuzzy & Conditional Matching: | |
Advanced data matching based on fuzzy matching or conditional matching supports complex business scenarios. |
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Reconciliation AI Agents: | |
iceDQ provides an AI agent to automatically create reconciliation rules based on ETL code, mapping document, etc. |
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FAQs: Data Reconciliation Tool with iceDQ
Why does SQL based Data reconciliation not work?
SQL cannot operate across databases or files, and join‑based diffing breaks when duplicates exist. SQL scripts demand ongoing manual maintenance and cannot generate structured exception reports or preserve an audit trail.
Why is sample-based reconciliation not enough for enterprise data pipelines?
Sample-based reconciliation leaves critical errors in untested records undetected. In enterprise pipelines, a single missed error can cascade into incorrect financial reports or compliance failures. iceDQ runs full volume reconciliation across every row and column without sampling, ensuring no error goes undetected regardless of data volume.
What is the difference between data observability and data reconciliation?
Data observability works on a single database and aggregate statistical properties like row count trends and schema drift to detect anomalies. Data reconciliation performs deterministic value-level comparison between source and target producing a precise exception report. iceDQ supports both automated reconciliation and production data monitoring in a single platform.
What is data reconciliation in ETL?
Data reconciliation in ETL verifies that every record was loaded correctly, all transformations produced the expected output, and no data was lost or duplicated after an ETL job runs. iceDQ automates this at full volume across record counts, column values, and aggregate totals in a single scheduled execution.
What are use cases of data reconciliation testing?
Common use cases include ETL pipeline validation, data migration certification, data warehouse layer reconciliation, Medallion architecture testing, BI report vs. source validation, and financial and compliance reconciliation for FINRA and BCBS-239. iceDQ supports all of these within a single platform.
Can iceDQ be integrated into a CI/CD or DataOps pipeline for automated reconciliation?
Yes. iceDQ provides a REST API to trigger reconciliation jobs programmatically as part of your CI/CD or DataOps pipeline. It integrates natively with orchestration tools like Airflow, Azure Data Factory, and dbt. Reconciliation results and exceptions are available via API for downstream reporting or alerting, enabling fully automated data certification without manual intervention.
What is the data recon framework for ETL QA?
A standard ETL QA reconciliation framework covers schema validation, row count reconciliation, column value comparison, aggregate validation, and transformation verification. iceDQ supports all five stages through no-code rule templates and produces a single consolidated exception report per execution.
How does iceDQ handle reconciliation between cloud and on-premises systems?
iceDQ supports 50+ connectors to reconcile data across on-premises systems like SAP and Oracle against cloud platforms like Snowflake and Azure Synapse within the same reconciliation job.
How does iceDQ handle reconciliation for Medallion architecture pipelines?
Yes, iceDQ reconciles data at every promotion step across bronze, silver, and gold layers enforcing standardization and cleansing rules at Bronze to Silver and validating business transformations and aggregations at Silver to Gold. iceDQ integrates with Airflow and ADF to trigger reconciliation jobs automatically at each layer promotion.
How does iceDQ generate reconciliation rules automatically?
iceDQ offers three ways to auto-generate rules. The Bulk Rule Generator creates rules across entire schemas in a single interaction. The MCP server generates rules from an uploaded mapping document. The AI-assisted check creation generates rule configurations from a natural language description of your reconciliation requirement.