Leveraging AI and ML for Predictive Monitoring and Error Mitigation in Change Data Capture Pipelines
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V6I3P116Keywords:
Change Data Capture, Data Pipelines, Predictive Monitoring, Anomaly Detection, Data Integrity, Observability, Automated Remediation, Root Cause Analysis, Concept DriftAbstract
Change Data Capture pipelines are widely used to propagate database changes into event streams, analytical stores and operational read models with low latency. As enterprises expand the number of source databases, connectors, downstream consumers and serving systems, operational reliability becomes harder to sustain. Failures that begin as a minor lag increase or a subtle schema evolution can cascade into missed records, duplicated events, inconsistent materialized views and downstream business defects. Traditional monitoring based on static thresholds and manual triage struggles because CDC behavior is non stationary, highly correlated across components and sensitive to workload shifts and change management practices. This paper proposes an architecture that embeds adaptive intelligence into CDC operations through predictive monitoring, anomaly detection, diagnosis and guarded automation. The approach fuses three families of signals. The first family is pipeline telemetry such as connector lag, throughput, offsets, retries and backpressure. The second family is data integrity signals such as row count deltas, key uniqueness checks and reconciliation between source and sink. The third family is change signals such as deploys, connector configuration edits and schema registry events. Lightweight models learn baselines and predict near term risk for lag growth, event loss and replication divergence. A graph based diagnosis method constrains root cause search using CDC topology and lineage and then ranks hypotheses using multi modal evidence including structured log templates. Finally, an action layer executes risk tiered mitigation steps such as auto scaling consumers, pausing downstream writes, triggering bounded replays and initiating snapshot repair with human approval gates for high impact actions. The paper outlines a prototype design and an evaluation plan using historical incident replay. It argues that the combination of predictive signals, topology aware diagnosis and policy based automation can reduce mean time to detection and mean time to recovery while improving trust in CDC driven data products
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