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

TL;DR

This paper introduces ACCORDANT, a domain-specific model combined with DevOps practices to improve the design, deployment, and monitoring of big data analytics architectures, addressing challenges of performance and complexity.

Contribution

It presents a novel domain-specific model and DevOps approach tailored for BDA architectures, enhancing deployment speed and performance monitoring capabilities.

Findings

Reduced deployment and monitoring times

Higher gain factor per iteration

Demonstrated generalization across domains

Abstract

Big data analytics (BDA) applications use machine learning algorithms to extract valuable insights from large, fast, and heterogeneous data sources. New software engineering challenges for BDA applications include ensuring performance levels of data-driven algorithms even in the presence of large data volume, velocity, and variety (3Vs). BDA software complexity frequently leads to delayed deployments, longer development cycles and challenging performance assessment. This paper proposes a Domain-Specific Model (DSM), and DevOps practices to design, deploy, and monitor performance metrics in BDA applications. Our proposal includes a design process, and a framework to define architectural inputs, software components, and deployment strategies through integrated high-level abstractions to enable QS monitoring. We evaluate our approach with four use cases from different domains to demonstrate a high level of generalization. Our results show a shorter deployment and monitoring times, and a higher gain factor per iteration compared to similar approaches.