  Information Technology has become a critical component in various industries,
leading to an increased focus on software maintenance and monitoring. With the
complexities of modern software systems, traditional maintenance approaches
have become insufficient. The concept of AIOps has emerged to enhance
predictive maintenance using Big Data and Machine Learning capabilities.
However, exploiting AIOps requires addressing several challenges related to the
complexity of data and incident management. Commercial solutions exist, but
they may not be suitable for certain companies due to high costs, data
governance issues, and limitations in covering private software. This paper
investigates the feasibility of implementing on-premise AIOps solutions by
leveraging open-source tools. We introduce a comprehensive AIOps infrastructure
that we have successfully deployed in our company, and we provide the rationale
behind different choices that we made to build its various components.
Particularly, we provide insights into our approach and criteria for selecting
a data management system and we explain its integration. Our experience can be
beneficial for companies seeking to internally manage their software
maintenance processes with a modern AIOps approach.

Software Engineering;Artificial Intelligence