Automated system for configuring and maintaining data pipelines using API metadescriptors

Abstract

An automated system (100) for configuring and maintaining data pipelines using API metadescriptors, comprising: a parser module configured to extract structural metadata from API metadescriptors; a pipeline generation module that dynamically constructs data pipelines based on the extracted metadata; a connector and adapter management module configured to establish communication with data sources and targets; a data mapping and transformation module to match and convert data from source to target formats; a pipeline orchestration module for scheduling and managing pipeline execution; a self-healing maintenance module to detect and respond to changes or failures in API structure or performance; and a user interface dashboard for real-time interaction, visualization, and control of pipeline configurations and operations.

Description

[0001] The present invention relates to the field of automated data engineering and software integration. More specifically, it relates to systems for configuring, deploying, and maintaining data pipelines using structured API metadescriptors. This invention lies at the intersection of data infrastructure automation, API-driven architecture, and intelligent pipeline lifecycle management.

[0002] In modern data-driven environments, organizations rely heavily on data pipelines to move, transform, and integrate data across disparate systems and platforms. However, configuring and maintaining these pipelines is often a manual, complex, and error-prone process. Engineers must navigate inconsistent APIs, evolving data schemas, lack of documentation, and the absence of standardized pipeline blueprints, leading to deployment delays, frequent outages, and increased maintenance overhead.

[0003] Furthermore, data engineering teams often struggle with the scalability and adaptability of traditional pipeline configurations. When APIs change or new data sources are introduced, existing pipelines must be manually rewritten or reconfigured, increasing operational costs and slowing analytics workflows. The lack of automation in mapping API parameters, handling authentication, managing data formats, and orchestrating transformations represents a major bottleneck for continuous data operations and real-time processing.

[0004] To address these challenges, there is a clear need for a system that can intelligently interpret API metadescriptors and automatically create and maintain data pipeline configurations. Such a system would significantly reduce manual effort, improve consistency, and increase the resilience of the data infrastructure. By automating pipeline setup and maintenance using standardized metadata and rule-based logic, organizations can achieve faster time to value, reduce operational risks, and respond quickly to changes in their data ecosystem.

[0005] One goal of the present disclosure is to automate the end-to-end configuration of data pipelines using API metadescriptors.

[0006] Another objective of this disclosure is to reduce manual coding effort and operational overhead in data processing.

[0007] Another goal of this disclosure is to dynamically adapt to API changes without disrupting existing pipelines.

[0008] Another objective of the present disclosure is to support seamless integration with various data sources and destinations.

[0009] Another objective of this disclosure is to improve data accuracy through intelligent mapping and transformation.

[0010] Another object of the present disclosure is to improve reliability through self-healing and self-maintenance functions.

[0011] Another objective of this disclosure is real-time monitoring and control through a user-friendly dashboard.

[0012] Another objective of this disclosure is to accelerate the deployment and scalability of data infrastructure solutions.

[0013] Further objects and advantages of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0014] The present invention relates to the automation of data pipeline creation by interpreting structured API metadescriptors such as OpenAPI or Swagger. This eliminates the need for manual pipeline configuration, improving speed and consistency. This enables scalable integration of multiple APIs into data platforms.

[0015] Another embodiment of the present invention is that the pipelines are dynamically generated based on endpoint structures, data formats, and authentication mechanisms extracted from the descriptors. The system adapts to changing API designs without human intervention. This ensures long-term flexibility and resilience in data operations.

[0016] Another embodiment of the present invention is that the system includes a modular framework for managing connectors to various sources and targets. It supports REST APIs, SQL databases, cloud storage, and more. The adapters are automatically configured depending on the API type and target system, thus improving interoperability.

[0017] Another embodiment of the present invention is that the source data is intelligently mapped to the target schemas using transformation rules and AI-based logic. This reduces manual field mapping and ensures data accuracy and completeness. It also enables advanced transformations such as data enrichment and formatting.

[0018] Another embodiment of the present invention is the continuous monitoring of APIs for schema changes, authentication issues, or performance degradation, whereby the system can automatically adapt pipelines. It regenerates the affected components or notifies users when manual input is required. This reduces downtime and operational overhead.

[0019] In another embodiment of the present invention, the centralized dashboard allows users to upload descriptors, review generated pipelines, adjust mappings, and monitor execution status. Role-based access and version control are integrated. The intuitive interface improves user control without requiring in-depth technical intervention.

[0020] Another embodiment of the present invention is that the invention provides a robust end-to-end solution for automating data integration tasks in complex, distributed systems. It supports multi-source and multi-target environments with minimal human dependency. This enables faster insights and a shorter time to value in data-driven organizations. API meta descriptor parser module:

[0021] This module serves as the base component of the system. It ingests structured API metadescriptors (such as OpenAPI, Swagger, RAML, or custom schema definitions) and interprets them to extract relevant information such as endpoints, request / response structures, authentication requirements, and data types. The parser converts these specifications into internal representations that can be used to automatically generate pipeline configurations. It ensures compatibility with evolving API standards and validates the completeness of the descriptors before further processing. Module for dynamic pipeline generation:

[0022] Using the parsed meta-descriptor data, this module automatically constructs end-to-end data pipelines tailored to the API structure. It defines data extraction routines, transformation logic, validation rules, and loading strategies based on a library of templates and predefined logic blocks. The engine supports various pipeline topologies such as batch, streaming, or hybrid models and dynamically adapts to the expected behavior of the API (e.g., paginated responses, webhook callbacks). Module for managing ports and adapters:

[0023] This module provides a robust framework for integration with various data sources and targets, including REST APIs, databases, file systems, cloud storage, and analytics platforms. It manages reusable connectors and protocol adapters that abstract the intricacies of communication and data processing. The module enables plug-and-play connectivity by automatically selecting and configuring appropriate adapters based on the metadescriptor input and the target data destination. Data mapping and transformation module:

[0024] This module is critical for ensuring data integrity and usability and handles schema matching, field-level transformation, and data enrichment. It uses rule-based mapping engines and AI-assisted inference techniques to map source API fields to target schema fields. The transformation logic can include format conversions, field renaming, default value assignments, and conditional calculations. This module also manages versioning and rollback of transformation rules as APIs evolve. Module for orchestrating and executing pipelines:

[0025] This module is responsible for scheduling, triggering, and monitoring pipeline executions. It integrates with orchestration frameworks (such as Apache Airflow, Dagster, or cloud-native schedulers) and supports event-driven, scheduled, and on-demand executions. It ensures reliability through retry mechanisms, error alerts, execution logs, and performance metrics. The module also supports parallelism and dependency chaining across multiple pipelines. Self-healing and maintenance module:

[0026] To reduce manual intervention, this module continuously monitors APIs for changes or errors and automatically adjusts pipelines accordingly. It detects schema mismatches, authentication errors, and performance bottlenecks and then applies corrective actions, such as regenerating pipeline steps, updating credentials, or notifying users. This intelligent maintenance approach ensures long-term operational stability and reduces the burden on data engineering teams. User interface and configuration dashboard:

[0027] This module provides a central graphical interface through which users can interact with the system, visualize pipelines, configure settings, and monitor performance. The dashboard allows users to upload API descriptors, review automatically generated mappings, customize transformation logic, and view real-time pipeline health metrics. It also provides audit logs, access controls, and integration with CI / CD pipelines for versioned deployments and collaboration.

[0028] The invention is explained again below with reference to the figure. It shows: Fig. : an automated system (100) for configuring and maintaining data pipelines using API meta-descriptors.

[0029] Fig.illustrates an automated system (100) for configuring and maintaining data pipelines using API metadescriptors. The system begins operation when a user uploads or links an API metadescriptor via the configuration dashboard, which is then parsed by the API metadescriptor parser module to extract endpoint structures, parameters, data types, and authentication protocols. This structured metadata is passed to the Dynamic Pipeline Generation Module, which uses predefined logic templates to automatically configure the ETL (extraction, transformation, and loading) components of a custom data pipeline. At the same time, the connector and adapter management module selects and configures appropriate connectors for the source API and target systems such as data lakes, warehouses, or visualization platforms.

[0030] The Data Mapping and Transformation module then maps the incoming data to the target schema using rule-based and AI-assisted mappings and applies the required conversions and enrichments. Once the pipeline is fully configured, the Pipeline Orchestration and Execution module schedules and executes the pipeline based on event triggers or time intervals, ensuring performance, logging, and error recovery. During runtime and after deployment, the Self-Healing and Maintenance module continuously monitors the pipeline, detecting API changes, credential issues, or schema mismatches, and autonomously updates configurations or alerts administrators.All of this is presented to users in real time via the user interface and configuration dashboard, which enables seamless visualization, editing, and control of all pipeline activities and metadata in a centralized and user-friendly environment.

Claims

[1] An automated system (100) for configuring and maintaining data pipelines using API metadescriptors, comprising: a parser module configured to extract structural metadata from API metadescriptors; a pipeline generation module that dynamically constructs data pipelines based on the extracted metadata; a connector and adapter management module configured to establish communication with data sources and targets; a data mapping and transformation module to match and convert data from source to target formats; a pipeline orchestration module for scheduling and managing pipeline execution; a self-healing maintenance module to detect and respond to changes or failures in API structure or performance; and a user interface dashboard for real-time interaction, visualization, and control of pipeline configurations and operations. [2] The system (100) of claim 1, wherein the parser module supports API metadescriptors in formats including, but not limited to, OpenAPI, Swagger, RAML, and custom JSON / YAML schemas. [3] The system (100) of claim 1, wherein the pipeline generation engine uses predefined logic templates and rule libraries to configure extraction, transformation, and loading operations. [4] The system (100) of claim 1, wherein the connection and adapter management module includes reusable protocol adapters for REST, SOAP, SQL, FTP, and cloud-based storage services. [5] The system (100) of claim 1, wherein the data mapping and transformation module uses AI-based inference techniques to automatically map source fields to target schemas. [6] The system (100) of claim 1, wherein the pipeline orchestration module is integrated with third-party orchestration tools such as Apache Airflow, Dagster, or cloud-native schedulers. [7] The system (100) of claim 1, wherein the self-healing maintenance module detects schema deviations, API authentication errors, and connectivity issues and autonomously updates the affected pipeline components. [8] The system (100) of claim 1, wherein the user interface dashboard provides functionality including descriptor upload, mapping customization, execution monitoring, access control, and audit logging.

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