Abstract
This paper explores the implementation of Elasticsearch as a primary data store for fast, distributed financial service applications. It examines the advantages of Elasticsearch in the context of financial services, including its scalability, high performance, flexible schema, and powerful search and analytics capabilities. The study addresses key challenges encountered during implementation, such as eventual consistency, limited join capabilities, and field limits, providing practical solutions based on real-world experience. The paper also discusses implementation strategies, focusing on data modeling for complex financial instruments, query optimization, and performance tuning. By balancing Elasticsearch's strengths against its limitations, this case study demonstrates how financial service organizations can leverage this technology to build more scalable, performant, and adaptable systems capable of handling the increasing data volumes and real-time processing demands of modern finance.
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