A system for the dynamic orchestration of data pipelines in a cloud environment

Abstract

A dynamic data pipeline orchestration system in a cloud environment, comprising: a workflow orchestration module configured to manage data pipeline execution using dynamic scheduling, event-driven triggers, and dependency-based execution strategies; an AI-driven resource allocation module configured to dynamically provision compute, storage, and network resources based on real-time workload fluctuations and demand forecasts; and a fault detection and recovery module configured to monitor pipeline execution, detect anomalies, and implement intelligent fault handling mechanisms, including automatic retries and workflow redirection.a multi-cloud and hybrid cloud integration module configured to enable seamless workload migration and resource allocation across multiple cloud service providers; a real-time data flow optimization module configured to analyze historical and real-time data trends to adjust execution plans, optimize ETL / ELT workflows, and reduce latency; a security and compliance module configured to ensure data integrity through encryption, authentication, and access control mechanisms while maintaining audit logs; a monitoring and visualization module configured to provide a real-time dashboard for pipeline tracking, performance analysis, and predictive analytics;and a module for cost optimization and automatic scaling, configured to minimize operating costs through workload-aware scheduling, serverless execution, and adaptive resource provisioning.

Description

The present invention relates to cloud-based data management and processing systems, and more particularly to a dynamic data pipeline orchestration system that provides optimized, real-time, and automated handling of various workloads in a cloud environment.Orchestration of data pipelines in cloud environments is essential for automatizing data collection, conversion, and processing in distributed infrastructures. Conventional systems rely on predefined scheduling, manual resource provisioning, and rigid workflow configurations, making them inefficient in handling dynamic and bulky cloud environments. Conventional data pipeline orchestration frameworks typically use cron jobs, directed acyclic graphs (DAGs), or rule-based triggers to execute workflows. These methods, although suitable for structured tasks, cannot be adapted to fluctuations in the workload in real time, unexpected data flooding, and system failures.Existing systems also require manual operations in resource allocation, resulting in inefficient use of cloud resources, increased operating costs, and performance bottleneckes. The lack of smart mechanisms for fault handling results in pipeline interrupts, data loss, and increased processing latencies because conventional retry mechanisms cannot dynamically redirect workflows or predict real-time failures. Moreover, conventional orchestration solutions are often vendor specific, which limits support of multiple clouds and makes migration of workloads complex. This vendor dependency prevents companies from optimizing costs and resource allocation across different cloud providers.Another limitation of conventional orchestration of data pipelines is the dependence on static data processing logic that does not optimize workflows based on real-time dataflow patterns. This leads to inefficient execution, high computational effort and delays in time-critical analyses. Moreover, conventional systems are not designed for dynamic scaling, resulting in inefficiencies in handling varying workloads and bulky data processing requirements. In view of these challenges, there is a need for a smart and adaptive system that enables orchestration of data pipelines in real-time with automatic resource allocation, dynamic workflow execution, and fault tolerance to improve reliability and efficiency in cloud environments.To solve this problem, the present invention provides a system for dynamic data pipeline orchestration in a cloud environment.The system for dynamic orchestration of data pipelines in a cloud environment enables scheduling and execution of data pipelines in real-time based on workload fluctuations, system constraints, and priority-based dependencies.The system for dynamically orchestration data pipelines in a cloud environment may dynamically provide computing, storage, and network resources to optimize cloud resource utilization and minimize costs.The system for dynamic data pipeline orchestration in a cloud environment may develop a fault tolerant system with proactive fault detection, automatic retry, and intelligent redirection to ensure uninterrupted pipeline execution.The system for dynamically orchestrating data pipelines in a cloud environment may provide seamless integration across multiple cloud platforms so that enterprises may dynamically migrate their workloads and avoid binding to a particular provider.The system for dynamic data pipeline orchestration in a cloud environment may utilize AI-based predictive analyses to optimize data processing workflows by analyzing historical workload trends and pre-emptionally adjusting pipeline execution strategies.The system for dynamically orchestrating data pipelines in a cloud environment supports elastic scaling, high performance computers, and distributed data processing to efficiently handle large workloads.The system for dynamic data pipeline orchestration in a cloud environment may provide an intuitive user interface and API gateway for configuring, monitoring, and modifying data pipeline workflows with minimal technical knowledge.The system for dynamic data pipeline orchestration in a cloud environment may provide data integrity, access control, and compliance with regulations through integrated security functions such as encryption, authentication, and audit logging.The system for dynamic data pipeline orchestration in a cloud environment may minimize processing delays through event driven execution, real-time orchestration, and self-healing mechanisms for high system availability.In one embodiment, a system is provided for dynamic data pipeline orchestration in a cloud environment. The dynamic data pipeline orchestration system in a cloud environment intelligently manages data workflows, optimizes resource usage, and improves fault tolerance. Unlike conventional static scheduling methods, the proposed system dynamically adjusts to workload fluctuations, system constraints, and real-time data processing requirements. The system uses AI-controlled resource allocation to automatically provide computational, storage, and network resources based on current demand, thus ensuring cost efficiency and optimal performance. It has intelligent fault detection and recovery mechanisms that allow proactive fault handling, automatic retry, and dynamic redirection to maintain seamless pipeline execution. Moreover, the invention supports multi-cloud and hybrid cloud environments so that companies can avoid binding to a particular provider.The invention is explained again below with reference to the figure. The following shows: FIG. 1 : shows a system for dynamic data pipeline orchestration in a cloud environmentFIG. 1 illustrates a system for dynamic data pipeline orchestration in a cloud environment. The dynamic data pipeline orchestration system in a cloud environment includes a workflow orchestration module, an AI-controlled resource allocation module, an error detection and recovery module, a multi-cloud and hybrid cloud integration module, a real-time data flow optimization module, a security and compliance module, a monitoring and visualization module, and a cost optimization and automatic scaling module. The workflow orchestration module is configured to manage execution of data pipelines using dynamic scheduling, event-driven triggers, and dependency-based execution strategies. The AI-controlled resource allocation module is configured to dynamically provide compute, storage, and network resources based on real-time workload fluctuations and demand predictions. The fault detection and recovery module is configured to monitor pipeline execution, detect anomalies, and implement intelligent fault handling mechanisms, including automatic retry and workflow redirection. The multi-cloud and hybrid cloud integration module is configured to allow seamless workload migration and resource allocation across multiple cloud service providers. The real-time data flow optimization module is configured to analyze historical and real-time data trends to adapt execution schedules, optimize ETL / ELT workflows, and reduce latencies. The security and compliance module is configured to ensure data integrity through encryption, authentication and access control mechanisms while maintaining auditing protocols. The monitoring and visualization module is configured to provide a real-time dashboard for pipeline tracking, performance analysis, and predictive analyses. The cost optimization and automatic scaling module is configured to minimize operating costs through workload aware scheduling, serverless execution, and adaptive resource provisioning. The workflow orchestration module dynamically adjusts pipeline execution to the real-time data volume, processing requirements, and system constraints.The AI-controlled module for resource allocation uses machine learning algorithms to predict patterns of resource usage and optimize provisioning. The fault detection and recovery module implements a self-recovery mechanism to automatically recover from detected anomalies and ensure seamless pipeline execution. The multi-cloud and hybrid cloud integration module dynamically migrates workloads between cloud platforms based on cost, performance, and availability factors. The real-time data flow optimization module uses AI-based predictive analyses to optimize execution order, parallel processing, and workflow dependencies. The security and compliance module implements access control policies, data encryption and legal compliance mechanisms. The monitoring and visualization module generates automatic warnings of failures, suboptimal performance, and resource anomalies. The cost optimization and automatic scaling module uses spot instances and pre-emptible virtual machines to optimize cloud outputs.List of reference characters100 System

Claims

A system for dynamic data pipeline orchestration in a cloud environment, comprising: a workflow orchestration module configured to manage execution of data pipelines using dynamic scheduling, event-driven triggers, and dependency-based execution strategies; an AI-driven resource allocation module configured to dynamically provide compute, storage, and network resources based on real-time workload fluctuations and demand predictions; an error detection and recovery module configured to monitor pipeline execution, detect anomalies, and implement smart error handling mechanisms including automatic retries and workflow redirection; a multi-cloud and hybrid cloud integration module configured to allow seamless workload migration and resource allocation across multiple cloud service providers; a real-time data flow optimization module configured to analyze historical and real-time data trends to adapt execution plans, optimize ETL / ELT workflows, and reduce latencies; a security and compliance module configured to ensure data integrity through encryption, authentication, and access control mechanisms while maintaining test protocols; a monitoring and visualization module configured to provide a real-time dashboard for pipeline tracking, performance analysis, and predictive analyses; and a cost optimization and automatic scaling module configured to minimize operating costs through workload aware scheduling, serverless execution, and adaptive resource provisioning.The system of claim 1, wherein the workflow orchestration module dynamically adjusts pipeline execution based on the real-time data volume, the processing requirements, and the system constraints.The system of claim 1, wherein the AI-controlled resource allocation module uses machine learning algorithms to predict resource usage patterns and optimize provisioning.The system of claim 1, wherein the fault detection and recovery module implements a self-recovery mechanism to automatically recover from detected anomalies and ensure uninterrupted pipeline execution.The system of claim 1, wherein the multi-cloud and hybrid cloud integration module dynamically migrates workloads between cloud platforms based on cost, performance, and availability factors.The system of claim 1, wherein the real-time data flow optimization module employs AI-based predictive analyses to optimize execution order, parallel processing, and workflow dependencies.The system of claim 1, wherein the security and compliance module passes through access control policies, data encryption, and legal compliance mechanisms.The system of claim 1, wherein the monitoring and visualization module generates automatic warnings of failures, suboptimal performance, and resource anomalies.The system of claim 1, wherein the cost optimization and autoscaling module uses spot instances and presetable virtual machines to optimize cloud outputs.

Images