Migration of AWS Redshift to Google BigQuery with BigLake
Author(s): Suhas Hanumanthaiah
Publication #: 2507037
Date of Publication: 12.11.2023
Country: United States
Pages: 1-7
Published In: Volume 9 Issue 6 November-2023
DOI: https://doi.org/10.5281/zenodo.16501221
Abstract
As organizations adopt multi-cloud strategies to optimize performance, cost, and flexibility, seamless migration between cloud data warehouse platforms becomes critical. This research explores the comprehensive process of migrating from Amazon Redshift to Google BigQuery, emphasizing the use of BigLake to enable secure, efficient, and scalable data access without relying on traditional Extract, Transform, Load (ETL) processes. The study outlines the architectural principles, phased migration strategy, implementation methodology, and automation techniques necessary for a successful transition. It highlights the use of BigLake external tables and secure BigQuery Omni VPN connections to directly access and query data stored in Amazon S3, significantly reducing network egress costs and development overhead. Practical considerations for handling complex data types, regional constraints, and schema conversions are addressed, along with a comparison of traditional migration approaches using ETL tools versus the proposed BigLake-based model. The paper demonstrates that using BigLake can cut developer effort by several weeks, reduce migration costs by minimizing data movement, and maintain compliance through secure, trusted pathways. The findings advocate for BigLake as a strategic solution for enterprises aiming to modernize their data architecture and leverage cloud-native analytics in a hybrid or multi-cloud environment.
Keywords: Cloud Data Migration, Amazon Redshift, Google BigQuery, BigLake, Multi-Cloud Strategy, Data Warehouse Modernization
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