# Online and Offline Analysis of Streaming Data

arXiv: https://arxiv.org/abs/1805.01021 · HTML: https://mlsanity.com/paper/1805.01021/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2018.

## Built on by (most cited first, 1 of 1)

- [Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset](https://mlsanity.com/paper/2411.09047/) (2024)

## Abstract (arXiv)

Online and offline analytics have been traditionally treated separately in software architecture design, and there is no existing general architecture that can support both. Our objective is to go beyond and introduce a scalable and maintainable architecture for performing online as well as offline analysis of streaming data. In this paper, we propose a 7-layered architecture utilising microservices, publish-subscribe pattern, and persistent storage. The architecture ensures high cohesion, low coupling, and asynchronous communication between the layers, thus yielding a scalable and maintainable solution. This design can help practitioners to engage their online and offline use cases in one single architecture, and also is of interest to academics, as it is a building block for a general architecture supporting data analysis.