International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Implementing Low-Latency Data Streaming from SQL Server to BigQuery: A Kafka-Based Approach in Google Cloud Platform

Author(s) Sainath Muvva
Country USA
Abstract This research paper presents an innovative approach to achieving near-real-time data synchronization between on-premise SQL Server databases and Google BigQuery, meeting the increasing need for timely analytics in contemporary data architectures. The proposed solution utilizes Apache Kafka as the central messaging system and Debezium as the Change Data Capture (CDC) connector, enabling low-latency data streaming with high scalability and resilience. Our architecture efficiently captures, processes, and transforms data before ingesting it into BigQuery, with updates reflected within an hour. The paper delves into the nuances of system design, implementation strategies, and optimization techniques, addressing challenges such as schema evolution, data consistency, and error handling. Through rigorous performance analysis and scalability testing, we demonstrate the efficacy of this Kafka-based approach in handling high-volume transactional data streams while leveraging BigQuery's powerful analytics capabilities. Additionally, the paper provides a detailed rationale for choosing Debezium as the CDC connector and Apache Kafka over alternatives like Google Cloud Pub/Sub, exploring the trade-offs and benefits of these technological choices. This comprehensive solution offers organizations a robust and cost-effective method to bridge on-premise databases with cloud platforms, facilitating advanced real-time analytics and data-driven decision-making in the cloud era.
Keywords Debezium, Kafka, BigQuery, SQL Server, kafka topic, CDC
Field Lainnya
Published In Volume 4, Issue 4, July-August 2022
Published On 2022-08-23
DOI https://doi.org/10.36948/ijfmr.2022.v04i04.25653
Short DOI https://doi.org/g82h45
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E-ISSN 2582-2160
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