Data Migration at Scale for Distributed Systems: Hot and Cold Migration (HCM)

Abstract

The process of transferring data from one system to another, known as data migration, is a critical task. As big data continues to grow, organizations encounter increasing complexity in managing data migration. While Apache Spark is an effective open-source big data processing framework that provides a versatile platform for data migration, there are also other tools and frameworks available, such as Apache NiFi, Kafka, Hadoop, and Amazon Warehouse Services (AWS) Glue. This paper explores data migration using Apache Spark alongside other widely used tools and frameworks, offering a comprehensive overview of each tool, and highlighting their strengths and weaknesses. The study includes a real-world performance-based case study, evaluating the data migration capabilities of each tool and providing detailed statistics for comparison. The results demonstrate that Apache Spark surpasses the other tools in terms of data transfer rates, processing times, and fault tolerance capabilities. Additionally, this paper refers to a randomized online algorithm to optimize costs for user-generated data stored in clouds with hot or cold migration, without requiring any future information. The
algorithm achieves a guaranteed competitive ratio and can be extended with prediction windows when short-term predictions are reliable.