Meshynix Lakehouse Reconciliation
A configuration-driven framework for automating data reconciliation in your Databricks Lakehouse - designed to improve data reliability and trust while reducing operational overhead. It enables timely detection of data anomalies, maximizes efficiency and runs natively on Databricks, built specifically for Databricks.
Reconciliation Accelerator: Works for all Industries
This built-on Databricks Reconciliation Accelerator is a configuration-driven framework that performs reconciliation across datasets from different catalogs and schemas within the Lakehouse, validating data quality across all layers of the Medallion Architecture based on user-defined rules.
The framework enables early detection of data anomalies, preventing thousands of hours of data downtime - eliminating delays for business users while data engineers and analysts troubleshoot and repair broken pipelines.
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Banking & Financial Services
• Independently Reconcile data across data sources, catalogs, schemas, financial ledgers or federated sources
• Automate reconciliation checks between data sources and improve data trust
• Detect anomalies in critical data elements between medallion layers or data sources -
Healthcare
• Perform data quality checks on critical medical data
• Automate data reconciliation between data sources ensuring internally consistent data
• Automatically Detect data anomalies before passing onto a downstream system -
Retail & e-Commerce
• Reconcile inventory levels across ERP, POS, and e-commerce platforms
• Track inconsistencies in order, shipment, stock and returns
• Detect pricing, discount, or tax mismatches in sales data -
Life Science
• Reconcile clinical trial data between CROs, labs, and internal systems
• Detect protocol deviations or inconsistent patient-level records
• Perform continuous and automated data quality check and generate alerts upon breach of defined threshold
Key Features
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Configurable Reconciliation Rules
Easily configure new data reconciliation rules between datasets or within a dataset
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Two‑Way and Three‑Way Support
Perform reconciliation between different medallion layers :- Bronze → Silver → Gold
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Metadata‑Driven Configuration
Define datasets, tables, layers, tolerances, aggregation rules via YAML or Excel files
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Audit Logging & Reporting
Detailed logs in Delta tables for matched/unmatched records, thresholds, timestamps, execution metadata
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Threshold‑Based Alerts
Configure Slack, email, webhook notifications when variance thresholds are exceeded
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Highly Scalable and Parallel Processing
Built-on Databricks leveraging the best capabilities of the platform for highly scalable and parallel processing
