Cross-Platform Reconciliation
Data Matching Software for Seamless Cross-Platform Reconciliation
DataBuck automates data matching and reconciliation using agentic AI and no-code ML, helping teams detect mismatches, validate data movement, and prove data accuracy across complex enterprise environments.
Trusted by the World’s Leading Enterprises
Helping teams across finance, telecom, healthcare, and supply chain streamline data matching and reconciliation.
What Customers Are Saying About Us
First Eigen has helped us tremendously with our sales attribution. Their data solutions are precise and consistent, and the team is great to work with. The confidence we have in First Eigen's data solutions has allowed us to focus on other areas of our business. We highly recommend First Eigen to any organization looking to elevate their data accuracy and performance.
DataBuck has been instrumental in ensuring data quality on our Hadoop platform. Its automated profiling and validation features make it easy to identify issues quickly and maintain trust in our data, and the user-friendly interface and flexible rule engine greatly accelerates data quality initiatives. I would highly recommend DataBuck for any organization looking to strengthen their data quality processes.
DataBuck by FirstEigen is a powerful, ML-driven data quality tool that not only automated complex validation tasks at scale but also integrated seamlessly with our GCP environment, significantly improving data trust while reducing manual effort by 50%.
DataBuck's automated data quality validation capability was used to validate sales data of the US Commercial operations. Its DQ rules recommendation engine can significantly reduce manual data validation efforts, improve issue detection, and enhance confidence in downstream analytics and reporting. DataBuck's scalability and improved transparency to data trust make it a valuable asset in any complex data environment.
Cell-to-Cell Match
Pinpoint Every Mismatch, Down to the Cell
DataBuck reconciles source and target tables row-by-row and cell-by-cell, surfacing mismatched records and trends in a single, real-time dashboard. DataBuck · Cell-to-Cell Match Dashboard
The highlighted matching chart surfaces full-match vs. mismatch rates at a glance — the foundation of faster, more accurate reconciliation.
Faster Reconciliation. Higher Accuracy.
DataBuck’s autonomous reconciliation replaces manual scripts with measurable results across the enterprise.
10x
Faster Processing
Faster than traditional matching tools
90%
Less Manual Effort
Autonomous ML handles the heavy lifting
99.9%
Match Accuracy
Confidence-scored reconciliation results
Why Traditional Data Matching Breaks at Enterprise Scale
Traditional data matching and reconciliation platforms create bottlenecks, delays, and validation errors.
Different data reconciliation software uses different schemas and business rules, creating constant manual mapping and validation work.
❌ Manual mapping across systems
High-volume data matching becomes an N² challenge, turning quick scripts into days or weeks of reconciliation.
❌ Slow data reconciliation processes
Formats, data types, and business logic change constantly, making rule-based matching difficult to maintain.
❌ Brittle matching rules break often
“DataBuck AI-powered data matching platform replaces manual scripts with autonomous matching that continuously adapts to changing data patterns and business rules.”
What DataBuck Matches Across Every Reconciliation Scenario
Not just records—it safeguards business truth. From simple field comparisons to complex many-to-many relationships, DataBuck autonomously handles every reconciliation pattern—without forcing you to hand-code every rule.
One dashboard for matched and mismatched tables, matching rates, and record-level trends across every scenario
Schema MatchingAuto Recommended
- Column name + format alignment
- Intelligent table mapping
Microsegment MatchingAuto Recommended
- Record count matching
- Microsegment-level count reconciliation
Primary Key MatchingAI / BuckGPT recommended
- Composite key discovery
- Auto column mapping
- Learns transformation logic from patterns and feedback
Record linkage
- Links records across sources even when identifiers are inconsistent (often discussed in the market as “entity resolution”)
Fuzzy matching
- Handles imperfect identifiers (names, addresses, free text, codes)
Aggregate Matching AI / BuckGPT recommended
- Reconciles rollups, totals, and grouped values
- Supports tolerance-based aggregate matching for controlled variance
Cross Platform MatchingEnterprise-ready
- Data-in-motion matching
- Batch matching
All matching rules are automatically discovered by AI and continuously updated based on your data patterns
Use Cases Teams Buy DataBuck For
- Use DataBuck for data migration validation—confirm schema alignment, counts, aggregates, and transformations before cutover.
- Common scenario: Snowflake to Databricks data reconciliation during platform migration.
- Run ETL reconciliation between pipeline stages so transformation errors don’t silently propagate.
- Support aggregate + tolerance logic with audit-friendly outputs for accountable workflows.
- Improve cross-system consistency with fuzzy and linkage patterns where IDs don’t line up.
Ready to employ AI-Powered Data Matching?
See DataBuck automate matching and reconciliation across your platforms so migrations and pipelines ship with confidence.
How FirstEigen’s Enterprise Data Matching Software Works
Connect
source and target systems with least-privilege access
Select
datasets to compare (tables/files/streams)
Generate
recommendations for mappings, keys, and transformations
Validate
and tune (your feedback improves future matching)
Run
at scale and review mismatches/exceptions
Operationalize
results with alerts, exports, APIs, or remediation workflow
Works Across Your Entire Data Ecosystem
Reconcile and monitor enterprise data across SQL systems, cloud platforms, orchestration tools, and governance ecosystems.
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Databricks
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Snowflake
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BigQuery
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Redshift
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AWS S3
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Azure
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Cloudera
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SQL Server
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Oracle
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Postgres
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AlloyDB
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Teradata
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MongoDB
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Hive
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Mainframe
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IBM Db2 z/OS
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VSAM
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COBOL Copybooks
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dbt
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Airflow
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Azure Data Factory
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Unity Catalog
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Alation
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Collibra
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APIs & Webhooks
DataBuck Matches Data at Every Stage of Your Pipeline
Continuous reconciliation from source to consumption.
Source Systems
Oracle
SQL Server
Teradata
Ingestion
Informatica
Dbt
Kafka
Data Lakes
Bronze
Silver
Gold
Consumption
PowerBI
Tableau
ML Models
Autonomous remediation at every pipeline stage ensures data consistency and catches discrepancies before they impact downstream consumers
AI Data Reconciliation Software with Built-In Security
Automate data reconciliation with private deployments, audit-ready controls, and secure access management.
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• Least-privilege connectors
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• Column-level protections
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• Data masking support
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• Private VPC/VNet deployment
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• Customer-managed keys
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• Network isolation options
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• Audit trails
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• SSO/SAML, SCIM
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• Role-based access controls
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• On Prem
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• Cloud
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• SaaS