"Data Consistency and Replication Strategies in Cassandra and Kafka Ecosystems"

EasyChair Preprint no. 13862

Abstract

Ensuring data consistency and efficient replication are critical challenges in distributed systems, particularly within Cassandra and Kafka ecosystems. This research delves into the comparative analysis of data consistency models and replication strategies employed by Apache Cassandra and Apache Kafka, two prominent technologies in big data management and real-time processing. We explore the underlying mechanisms that each system utilizes to achieve high availability, fault tolerance, and eventual consistency, while balancing trade-offs in performance and data integrity. By examining real-world applications and case studies, this study provides insights into optimal configurations and best practices for deploying these systems in various scenarios, such as financial services, e-commerce, and IoT. The findings aim to guide developers and system architects in designing robust, scalable, and consistent data architectures.

Keyphrases: Apache Cassandra, Apache Kafka, Big Data, data consistency, distributed systems, eventual consistency, fault tolerance, real-time processing, Replication strategies

@Booklet{EasyChair:13862,
  author = {Alakitan Samad},
  title = {"Data Consistency and Replication Strategies in Cassandra and Kafka Ecosystems"},
  howpublished = {EasyChair Preprint no. 13862},

  year = {EasyChair, 2024}}