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eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in March 2026 (Volume 29, Issue 3) Submit manuscript

Intelligent Python Automation for Oracle GoldenGate Replication: Real-Time Lag Prediction, Anomaly Detection, and Self-Healing Using Machine Learning

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  • Intelligent Python Automation for Oracle GoldenGate Replication: Real-Time Lag Prediction, Anomaly Detection, and Self-Healing Using Machine Learning

Adithya Sirimalla *

Enlivien Technologies Inc, USA.
 
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 3334-3342
Article DOI: 10.30574/wjarr.2024.23.3.2979
DOI url: https://doi.org/10.30574/wjarr.2024.23.3.2979
Received on 16 August 2024; revised on 25 September 2024; accepted on 28 September 2024
 
Modern enterprise systems that demand real-time availability, low latency access to running data mandate the use of real time replication of data. These cases are common to Oracle GoldenGate where replication pipelines have unpredictable lag spikes, bursts of errors, as well as stalled processes that cannot be effectively handled by existing rule-based monitoring techniques. This paper demonstrates a smart Python-based automation model incorporating machine learning to predict lag in real-time, multivariate anomaly detection, and self-heal processes of GoldenGate. The system is a continuous collection of GoldenGate metrics and system telemetry as well as log events, which are converted into engineered features consumed by a gated recurrent unit (GRU) predictor and an Isolation Forest anomaly detector. In situations where the models detect or predict abnormal behavior, the framework initiates specific self-healing measures like Replicat restarts, trail validation or parameter tuning. As experimental assessment of a controlled Goldensimilar test environment indicates, the suggested solution is much better in terms of predictive accuracy, precision in anomaly detection, and mean time to recover than traditional monitoring features. The findings indicate that with the use of ML-based intelligence along with Python automation, it is possible to have proactive, adaptive and low-overhead operations management. The current study adds a cohesive architecture, which brings GoldenGate replication a step closer to autonomous AIOps-based reliability.
 
Oracle GoldenGate; Machine Learning Automation;  Self-Healing Systems; Python Automation; GRU Model; Isolation Forest; AIOps
 
https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2024-2979.pdf

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Adithya Sirimalla. Intelligent Python Automation for Oracle GoldenGate Replication: Real-Time Lag Prediction, Anomaly Detection, and Self-Healing Using Machine Learning. World Journal of Advanced Research and Reviews, 2024, 23(3), 3334-3342. Article DOI: https://doi.org/10.30574/wjarr.2024.23.3.2979

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