Real-time data synchronization between streaming and relational systems

Abstract

A real-time data synchronization between streaming and relational systems (100), comprising: a) a data ingestion and stream connection module configured to receive, normalise and format high-speed data from one or more streaming sources; b) a relational data snapshot and access module configured to retrieve structured data snapshots from one or more relational databases; c) a data mapping and schema harmonization module adapted to align and transform data schemas between the streaming sources and the relational databases; (d) a real-time reconciliation engine configured to compare, reconcile, and identify discrepancies between the streaming data and the relational data in real time, using one or more predefined or AI-based rules; (e) a conflict resolution and action module configured to apply corrective actions based on detected discrepancies using user-defined, rule-based or automated strategies; (f) a monitoring and alerting interface adapted to display the reconciliation status, issue warnings in the event of errors or anomalies and enable user supervision; g) and a security, control and logging module configured to ensure data integrity, user access control, encryption and the maintenance of audit trails, h) whereby the system ensures consistent, accurate and synchronised data states across heterogeneous data platforms in real time.

Description

[0001] The present invention relates to the field of real-time data processing and integration technologies. In particular, it concerns systems for seamless data reconciliation between high-speed streaming data sources and traditional relational database systems. The invention addresses real-time data consistency, synchronization, and accuracy in heterogeneous data environments.

[0002] Streaming systems offer rapid data ingestion and real-time analytics capabilities, while relational systems enable structured storage, transactional integrity, and historical queries. However, maintaining consistency and synchronization between these two paradigms has become a major challenge due to differences in data formats, update frequency, latency tolerance, and schema evolution.

[0003] Current methods for data reconciliation between streaming and relational systems often involve regular batch jobs or custom connectors, which introduce latency, increase system complexity, and cause data integrity issues. These approaches are inadequate in scenarios where near-instant consistency is required, such as financial transactions, healthcare monitoring, supply chain logistics, or fraud detection. Furthermore, schema mismatches, duplicate entries, and delayed updates can lead to inaccurate reporting, regulatory noncompliance, and operational inefficiencies.

[0004] To address these limitations, there is a need for a robust real-time data reconciliation framework that can intelligently detect discrepancies, reconcile conflicts, and ensure a synchronized state in both streaming and relational data systems. The invention described here proposes an intelligent system that continuously monitors data streams, compares incoming stream records with relational data snapshots, and applies automated or rule-based reconciliation mechanisms to maintain data coherence, reduce latency, and improve decision accuracy across platforms.

[0005] One goal of this disclosure is to enable real-time synchronization between streaming and relational data systems.

[0006] Another objective of this disclosure is to reduce data latency and improve decision accuracy.

[0007] Another objective of the present disclosure is to automatically resolve data inconsistencies through intelligent conflict handling.

[0008] Another goal of this disclosure is to support dynamic schema mapping and evolution in heterogeneous sources.

[0009] Another objective of this disclosure is to provide complete audit trails for compliance and traceability.

[0010] Another objective of the present disclosure is to improve operational efficiency by avoiding delays in batch reconciliation.

[0011] The present invention relates to a real-time data reconciliation system that bridges streaming data sources and relational databases. It ensures synchronization and consistency between rapidly changing event streams and structured historical data.

[0012] Another embodiment of the present invention is that the system begins with a robust data entry module that captures and standardizes data from multiple real-time sources. This enables consistent downstream processing regardless of source diversity or format.

[0013] Another embodiment of the present invention includes a snapshot-based access mechanism for relational databases for retrieving the most recent structured data. This ensures that the comparison is performed against the most current and relevant database entries.

[0014] Another embodiment of the present invention is that the schema harmonization module reconciles different data formats between streaming and relational systems. It intelligently maps fields, resolves inconsistencies, and supports dynamic schema evolution.

[0015] Another embodiment of the present invention is that the real-time matching function compares incoming stream records with relational data snapshots. It detects discrepancies, anomalies, and inconsistencies using rule-based and AI-supported logic.

[0016] In another embodiment of the present invention, the conflict resolution module automates the handling of discrepancies through corrective, flagging, or queuing actions. Furthermore, each conflict resolution decision is logged to ensure traceability and compliance.

[0017] Another embodiment of the present invention is the monitoring and alerting dashboard, which visualizes the status of the reconciliation, anomaly trends, and system metrics. It actively notifies users of problems, enabling proactive intervention and continuous monitoring.

[0018] Another embodiment of the present invention is the integration of strong security and governance controls that ensure encrypted data flows, audit trails, and policy enforcement. This makes it suitable for industries with strict compliance and data integrity requirements. Data acquisition module and stream connector:

[0019] This module is responsible for continuously ingesting data from various real-time streaming sources such as IoT sensors, APIs, protocols, or messaging systems such as Apache Kafka, AWS Kinesis, or MQTT brokers. It normalizes the incoming data and converts it into a unified format suitable for subsequent processing. The module supports handling schema evolution, timestamps, and real-time data transformation to ensure compatibility with matching logic and relational systems. Module for snapshotting and accessing relational data:

[0020] This component interacts with traditional relational databases such as PostgreSQL, Oracle, MySQL, or MS SQL Server. It retrieves the most current and relevant data snapshots either regularly or based on triggers to ensure the data used for reconciliation reflects the most up-to-date state. The module is optimized for low-latency reads and supports versioned snapshots, incremental retrieval, and metadata synchronization. Data mapping and schema harmonization module:

[0021] This module addresses the complexity of schema matching between streaming data and relational databases. It automatically maps data fields, resolves naming discrepancies, applies type constraints, and enforces data formatting rules to ensure consistent schema structures. The module includes an intelligent mapping engine that learns from previous fixes and dynamically adapts mappings based on updates in source schemas. Real-Time Reconciliation Engine:

[0022] This is the central logic module that performs a live comparison between streaming data and relational database entries. It detects anomalies, inconsistencies, missing records, and duplicate entries using configurable rules, AI-driven heuristics, and timestamp matching. It supports both key-based and content-based matching and can operate in strict or relaxed consistency mode depending on the requirements of the use case. Conflict resolution and action module:

[0023] When discrepancies are detected, this module executes resolution strategies such as automatic correction, record flagging, update queues, or alerts. The resolution logic can be rule-based, user-defined, or AI-assisted, allowing for high flexibility in handling different types of discrepancies. It also maintains an audit trail of reconciliation decisions, supporting rollback and traceability for compliance and debugging purposes. Monitoring and alarm interface:

[0024] This module provides a central dashboard for administrators and analysts to view reconciliation status, detected issues, and performance metrics. It includes visualization tools for anomaly trends, data integrity assessments, and reconciliation latency. Integration with alerting systems (email, SMS, Slack) ensures proactive notification of reconciliation errors, schema changes, or data spikes. Security, Governance and Logging Module:

[0025] To ensure secure data handling, this module manages access control, encryption, and data masking policies. It also enforces data governance standards by labeling sensitive fields, applying data retention policies, and integrating with audit logging systems. Every transaction, comparison, and resolution event is logged with timestamps and user actions, enabling complete traceability and regulatory compliance.

[0026] The invention is explained again below with reference to the figure. It shows: Fig. : an illustration of real-time data synchronization between streaming and relational systems (100)

[0027] Fig.shows an illustration of real-time data synchronization between streaming and relational systems. The system initially works with the Data Ingestion and Stream Connector Module to continuously ingest, transform, and normalize data from various real-time sources such as sensors, APIs, or event streams like Kafka.

[0028] At the same time, the Relational Data Snapshots and Access module retrieves the latest snapshots from traditional relational databases, ensuring a synchronized view of historical and structured data. The Data Mapping and Schema Harmonization Module then reconciles the schema between the streaming and relational data sources, resolving field discrepancies and ensuring compatibility through intelligent mapping and type conversion. Once the data structures are harmonized, the real-time reconciliation engine performs high-speed comparisons of the incoming stream records with relational snapshots and identifies discrepancies such as missing records, duplicates, or inconsistent values ​​using deterministic rules and AI-based logic.In the event of a conflict, the Conflict Resolution and Actions module applies corrective actions—from automatic updates to user-configured resolution rules—while maintaining a complete audit trail of all decisions. The Monitoring and Alerting interface continuously visualizes reconciliation performance, flags anomalies, and sends proactive alerts to users so they can manually intervene as needed. Throughout the process, the Security, Governance, and Logging module provides encryption, access control, data reconciliation, and compliance logging to ensure the entire operation is secure, auditable, and compliant with the organization's data governance standards.

Claims

[1] A real-time data synchronization between streaming and relational systems (100), comprising: a) a data ingestion and stream connection module configured to receive, normalise and format high-speed data from one or more streaming sources; b) a relational data snapshot and access module configured to retrieve structured data snapshots from one or more relational databases; c) a data mapping and schema harmonization module adapted to align and transform data schemas between the streaming sources and the relational databases; (d) a real-time reconciliation engine configured to compare, reconcile, and identify discrepancies between the streaming data and the relational data in real time, using one or more predefined or AI-based rules; (e) a conflict resolution and action module configured to apply corrective actions based on detected discrepancies using user-defined, rule-based or automated strategies; (f) a monitoring and alerting interface adapted to display the reconciliation status, issue warnings in the event of errors or anomalies and enable user supervision; g) and a security, control and logging module configured to ensure data integrity, user access control, encryption and the maintenance of audit trails, h) whereby the system ensures consistent, accurate and synchronised data states across heterogeneous data platforms in real time. [2] The system (100) of claim 1, wherein the data ingress and stream connection module supports multiple streaming protocols including Kafka, Kinesis, MQTT, and REST-based APIs. [3] The system (100) of claim 1, wherein the relational data snapshot module and -Access performs version-controlled and incremental snapshot retrieval to reduce data latency and improve efficiency. [4] The system (100) of claim 1, wherein the data mapping and schema harmonization module uses machine learning to automatically correct field and schema deviations over time. [5] The system (100) of claim 1, wherein the real-time matching engine supports both key-based and content-based matching for detecting anomalies and conflicts. [6] The system (100) of claim 1, wherein the conflict resolution and action module maintains a time-stamped audit trail of all voting actions for regulatory and troubleshooting purposes. [7] The system (100) of claim 1, wherein the monitoring and alerting interface is integrated with external platforms such as Slack, email or SMS gateways for real-time notifications. [8] The system (100) of claim 1, wherein the security, governance, and logging module enforces field-level encryption, data masking, role-based access control, and compliance with industry data governance standards such as GDPR and HIPAA.

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