Data process by Batch systems - Monitoring Automation Using Artificial Intelligence

AI-powered batch monitoring revolutionizes data processing with automation and proactive issue detection

Govindaiah Simuni is passionate about solving enterprise data architecture problems, batch processing, and ensuring customers achieve their desired business outcomes. As a solution architect, he designs comprehensive solutions considering various elements, such as hardware, software, network infrastructure, data management, and batch systems.

As a Data Architect, Govindaiah assesses numerous technological possibilities and makes informed decisions based on compatibility, cost, and industry best practices. He oversees the implementation process, directs development teams, and manages technical issues. He also recommends technologies most appropriate for the organization's long-term strategy, ensuring that the implemented solution adheres to design principles, meets quality standards, and fulfills business requirements.

As an architect, he consistently investigates, identifies, and assesses risks associated with solutions, such as security vulnerabilities, data privacy concerns, and performance bottlenecks. He develops strategies to mitigate these risks and ensure the solution’s reliability and robustness. He continuously evaluates the implemented solution, gathers feedback, and identifies areas for improvement. Govindaiah stays updated with emerging technologies, industry trends, and best practices, incorporating them into future solution designs.

Batch Monitoring and Data Processing: Challenges & Solutions

Batch Job Monitoring:

One of the biggest challenges in processing ETL jobs is achieving error-free data and seamless end-to-end integration between applications. Introducing a system that can automatically run jobs, manage its dependencies, and does not require constant monitoring at every step would help reduce time constraints and minimize error-prone manual intervention. The overall benefits of such an automated system include improved processing efficiencies and fewer mistakes.

Manual Activity:

In addition to ensuring ETL jobs run smoothly, manual activities also involve meeting service-level agreements (SLAs), handling job failures, root cause detection, and manual file processing.

Challenges:

In large organizations, where real-time data must be available 24/7, jobs are scheduled to run across the company, which requires continuous human support around the clock.

●      The turnaround time for recovering from job failures is prolonged due to dependencies on human intervention and the collaboration of multiple teams.
●      Monitoring multiple jobs simultaneously requires significantly more resources, and the results may still not be accurate. For example, manually identifying long-running jobs among 1000+ parallel jobs can lead to missed SLAs.
●      When humans are engaged in mundane tasks like monitoring jobs day and night, their intellectual potential is not fully utilized. The repetitive nature of this work can also degrade job quality and lower employees’ interest in their roles.
●      Many organizations spend approximately 30% of their revenue on operations, primarily to maintain sufficient manpower just to keep systems running.

Scheduling Jobs: Jobs must be well-planned and scheduled according to data and time dependencies to avoid contention. While scheduling jobs can ensure the timeliness and accuracy of data, we cannot foresee failures and delays. Intelligence is needed to predict the runtime of a job on a particular day, making the process proactive rather than reactive.

Solution: Batch Automation Using AI/ML

The AI process uses machine learning in a systematic way to build a framework that monitors end-to-end batch processing systems. The system learns, heals, and improves itself over time. It monitors the structure, schedules, pace of the run, errors, and fixes of batch processes. The AI/ML system becomes more efficient over time, improving reliability, stability, and scalability.

Systems, computer program products, and methods are described for monitoring and automatically controlling batch processing. The AI-based process is configured to receive multiple data processing requests and determine a processing plan for these requests. Based on the processing plan, the AI system may provide actions for the processing applications to complete the requests for data processing. It determines the state of the data processing requests, using an event-state decision machine learning model to suggest remedial actions for error states. It also instructs the processing applications on performing the necessary remedial actions.

Batch Processing Overview:

Batch processing is a method of handling large volumes of data processing requests (e.g., jobs) with minimal or no user interaction. Typically, batch processing is scheduled to run when computing resources are available. Support teams manually monitor applications during batch processing and address any issues as they arise.

System for Monitoring and Controlling Batch Processing:

In one embodiment, a system for monitoring and automatically controlling batch processing includes at least one non-transitory storage device and at least one processing device coupled to the storage device. The processing device is configured to receive a plurality of data processing requests, along with a calendar, tasks to be completed, and requirements for each request. The system determines a processing plan for the requests, including the order of execution and computing resources needed, and provides actions for processing applications to complete these requests.

While the processing applications perform their actions, the system determines the state of the requests, identifying any error states. Using an event-state decision machine learning model, the system determines one or more remedial actions to resolve the error states. It provides instructions to the applications to perform those actions.

In some embodiments, the system scans a log of events occurring during processing to determine the state of the requests. The scanning process is based on one or more configured services. If an error state is identified, an incident management ticket may be generated, and the system will monitor whether the remedial actions resolve the issue. If no

Remedial Actions:

The system can take several actions to resolve errors, including:

●      Continuing to perform the actions to complete the requests.
●      Restarting the processing actions.
●      Pausing performance for a specified time period and resuming afterward.
●      Skipping some actions in the processing.
●      Fixing errors during processing.
●      Escalating the issue to a support user.
●      Stopping performance of certain actions.

n some embodiments, these actions are communicated to the processing applications via an application programming interface (API).

Computer Program Product for Monitoring and Controlling Batch Processing:

A computer program product for monitoring and controlling batch processing is described in another aspect. The product includes a non-transitory computer-readable medium with code that instructs a device to perform several actions:

Receive multiple data processing requests and determine each task, calendar, and requirement.

●      Generate a processing plan specifying the tasks and resources required.
●      Provide actions for processing applications to complete the requests.
●      Continuously monitor the state of the requests and identify any errors.
●      Use an event-state decision machine learning model to propose and provide instructions or remedial actions.

In some embodiments, the product further includes code to scan event logs to determine the state of requests, generate incident management tickets, and provide notifications to support users if the error state is unresolved.

Enhanced Batch Processing System with AI:

In some embodiments, the system is designed to train a semi-supervised learning algorithm using historical data associated with data processing requests, batch processing runs, interdependencies, tasks, actions, and outcomes. This event-state decision machine learning model outputs actions to resolve errors in batch processing. The system can:

Receive data processing requests.

●      Determine a processing plan.
●      Provide actions for processing applications.
●      Monitor the state of requests.
●      Suggest and implement remedial actions, including escalating issues to support users.

In the event of an error, the system can generate a notification using a contact list data structure to alert support users via their user devices.

Batch Processing System Overview:

A batch processing system involves data from various sources being fed into a central processing unit, monitored by an AI system. The AI system analyzes real-time data streams to identify anomalies and trigger automated responses. The key elements of this system include data ingestion, feature engineering, anomaly detection, alert generation, and automated remediation actions.

Key Components:

Data Ingestion:

Data from various sources (e.g., production systems, sensors, logs) is continuously collected and streamed into the monitoring system.

Data Preprocessing:

Raw data is cleaned, formatted, and transformed into a structured format suitable for analysis.

Feature Engineering:

Relevant features are extracted from the data to enable better anomaly detection by the AI model (e.g., average processing time, error rates, resource utilization).

AI Model (Anomaly Detection):

A machine learning model, such as an autoencoder or LSTM network, is trained to learn normal patterns in the data and identify deviations that could indicate issues.

Anomaly Scoring:

The AI model assigns a score to each data point, indicating the likelihood of it being an anomaly.

Alert Generation:

When anomalies are detected that exceed pre-defined thresholds, the system triggers alerts to notify relevant personnel or automatically initiates remediation actions.

Automated Response:

Based on the severity of the anomaly and configured rules, the system can automatically take actions such as restarting a batch job, scaling resources, or notifying operators.

Visual Representation:

Data Stream:

Depict a continuous flow of data from various sources entering the system.

Central Processing Unit:

A box representing the core AI engine that analyzes the data and generates alerts.

Data Visualization:

A dashboard displaying real-time metrics, anomaly detection results, and potential trends.

Feedback Loop:

Arrows show how automated actions or human intervention can be fed back into the system to adjust monitoring parameters and improve model performance.

Benefits of AI-based Batch Monitoring Automation:

Proactive Detection:

Early identification of potential issues before they escalate into major problems.

Reduced Manual Intervention:

Automated alerts and remediation actions minimize the need for human oversight.

Improved Efficiency:

Faster response times to anomalies, maximizing system uptime and throughput.

Adaptive Learning:

AI models can continuously learn from new data to improve anomaly detection accuracy over time.

Important Considerations:

Data Quality:

Ensuring accurate and consistent data is crucial for effective anomaly detection.

Model Training and Tuning:

Regularly evaluating and updating the AI model is essential to maintaining its performance.

Alert Management:

Setting appropriate thresholds and filtering alerts to avoid alert fatigue

Conclusion

AI-powered batch monitoring is revolutionizing data processing by automating workflows, reducing manual intervention, and enhancing efficiency. Machine learning and anomaly detection enable proactive issue resolution, minimizing errors and downtime. These systems continuously improve with adaptive learning, ensuring reliable and scalable batch operations. Organizations benefit from faster processing times, reduced operational costs, and improved overall performance, making AI-driven batch automation an essential tool for modern data management