AI-Driven Change Data Capture (CDC) In Bigquery Vs. Traditional Databases: A Comparative Analysis of Debezium, Google Spanner, And AI-Based Approaches
Keywords:
AI-driven Change Data Capture, BigQuery, Google Spanner, Debezium, schema evolution, anomaly detection, real-time data processing, predictive analytics, reinforcement learning, cloud-native databases.Abstract
The evolution of data ma0nagement has necessitated the development of efficient Change Data Capture (CDC) mechanisms to ensure real-time data synchronization across disparate systems. Traditional CDC methodologies, including log-based and trigger-based approaches, often face significant challenges related to latency, schema evolution, and resource consumption. These inefficiencies become more pronounced as organizations scale their data infrastructure to accommodate increasing transactional workloads. While cloud-native platforms such as Google BigQuery and Google Spanner offer enhanced data ingestion capabilities, they still require advanced techniques to mitigate schema drift, minimize processing overhead, and improve fault tolerance.
Despite the advancements in CDC technologies, existing approaches remain limited in their adaptability and efficiency. There is a notable gap in research focusing on the integration of artificial intelligence to enhance CDC processes, particularly in differentiating how AI-driven mechanisms perform in cloud-based databases compared to traditional database frameworks. This study seeks to address this gap by introducing a novel AI-enhanced CDC framework, leveraging machine learning techniques to optimize schema evolution, event anomaly detection, and real-time data consistency. By benchmarking BigQuery’s streaming ingestion, Google Spanner’s change streams, and Debezium’s log-based CDC, this research presents a comparative analysis to evaluate the impact of AI integration in CDC.
The findings of this study indicate that AI-driven CDC solutions significantly outperform conventional methods, demonstrating substantial improvements in reducing latency, enhancing anomaly detection accuracy, and optimizing computational resources. The research underscores the role of predictive analytics and reinforcement learning in CDC, showcasing their ability to automate schema management and refine event tracking processes. The study’s results highlight the transformative potential of AI-driven CDC, paving the way for more efficient, scalable, and intelligent data management solutions in modern enterprises.
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