Architectural Design Decisions for Self-Serve Data Platforms in Data Meshes

TL;DR

This paper presents a catalog of architectural design decisions for self-serve data platforms within data meshes, based on a systematic literature review and expert interviews, to guide practitioners and inform future research.

Contribution

It systematically compiles and validates architectural decisions and options for self-serve data platforms in data meshes, combining literature review and expert insights.

Findings

Catalog of 43 design decisions and options

Validated and extended through expert interviews

Provides a baseline for future research and practice

Abstract

Data mesh is an emerging decentralized approach to managing and generating value from analytical enterprise data at scale. It shifts the ownership of the data to the business domains closest to the data, promotes sharing and managing data as autonomous products, and uses a federated and automated data governance model. The data mesh relies on a managed data platform that offers services to domain and governance teams to build, share, and manage data products efficiently. However, designing and implementing a self-serve data platform is challenging, and the platform engineers and architects must understand and choose the appropriate design options to ensure the platform will enhance the experience of domain and governance teams. For these reasons, this paper proposes a catalog of architectural design decisions and their corresponding decision options by systematically reviewing 43 industrial gray literature articles on self-serve data platforms in data mesh. Moreover, we used semi-structured interviews with six data engineering experts with data mesh experience to validate, refine, and extend the findings from the literature. Such a catalog of design decisions and options drawn from the state of practice shall aid practitioners in building data meshes while providing a baseline for further research on data mesh architectures.