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Big data analytics (BDA) applications use machine learning algorithms toextract valuable insights from large, fast, and heterogeneous data sources. Newsoftware engineering challenges for BDA applications include ensuringperformance levels of data-driven algorithms even in the presence of large datavolume, velocity, and variety (3Vs). BDA software complexity frequently leadsto delayed deployments, longer development cycles and challenging performanceassessment. This paper proposes a Domain-Specific Model (DSM), and DevOpspractices to design, deploy, and monitor performance metrics in BDAapplications. Our proposal includes a design process, and a framework to definearchitectural inputs, software components, and deployment strategies throughintegrated high-level abstractions to enable QS monitoring. We evaluate ourapproach with four use cases from different domains to demonstrate a high levelof generalization. Our results show a shorter deployment and monitoring times,and a higher gain factor per iteration compared to similar approaches.

ACCORDANT: A Domain Specific Model and DevOpsApproach for Big Data Analytics Architectures