Case study
Enhanced Aviation Operations with Elasticsearch
How SquareShift's Elastic workshops helped an aviation MRO provider build a unified search foundation for maintenance logs, alerts, and anomaly detection.
Training a team on indices and streams only sticks when it's tied to their own maintenance logs — abstract Elastic workshops don't survive contact with a real incident.
Platform & Software Engineering Practice Lead, SquareShift
A prominent aviation services provider delivering maintenance, engineering, and modification support to commercial and military aircraft operators.
Its technical infrastructure has to support high reliability and operational efficiency at all times.
- Unified search foundation — Indices and data streams established for maintenance logs and workflows.
- Proactive monitoring — Alerts, watchers, and anomaly detection now in place.
- Unified visibility — Azure and Elastic features integrated end-to-end.
How SquareShift delivered it.
The challenge
An aviation services provider supporting commercial and military aircraft operators ran a complex service structure with no unified way to search or visualize maintenance logs and workflows. Teams had minimal prior exposure to Elastic features like Canvas, Fleet, and machine learning.
The client needed a real search and observability foundation — proactive monitoring, alerting, and a clear plan for using Elastic’s broader feature set, not just a tool switched on and left unused.
What we delivered
SquareShift established the client’s search foundation, working through the tradeoffs between indices and data streams, then led detailed workshops introducing the team to Elastic’s ecosystem — ML, Canvas, Fleet, and Logstash — grounded in the client’s own maintenance and operations data.
Sessions covered alerts, watchers, and anomaly detection, and the team integrated Private Link and Elastic Enterprise Search features to unify visibility across Azure and Elastic.
The payoff
The client now has a working search foundation for maintenance logs and workflows, with proactive monitoring and alerting in place instead of ad hoc log review.
Hands-on training on indices, streams, and anomaly detection means the client’s own team can extend the platform going forward, using demonstrated patterns like proactive maintenance alerts and secure REST APIs rather than starting from scratch each time.
Where this work sits