Case Study: Olameter achieves near real-time XML processing and faster predictive maintenance with Onehouse

Harnessing the Power of a Fully Managed Universal Data Lakehouse

Olameter Inc., a leading utility asset management and data services provider, faced a significant challenge in processing a near-decade-long backlog of complex XML meter reading data. This massive dataset was vital for their predictive maintenance systems, but their initial attempts using custom .NET applications were financially and logistically impossible, as it would take over six months to process just a single year's worth of information. This bottleneck prevented them from analyzing the data needed to predict power outages and map network topology.

Partnering with Onehouse, Olameter implemented its fully managed Universal Data Lakehouse to create a custom XML ingestion solution. Onehouse enabled Olameter to intelligently parse, flatten, and structure their data, reducing processing times from years to days and allowing for near real-time ingestion. This resulted in a 10x saving on compute costs and provided Olameter with the robust pipelines and machine learning models needed to gain critical operational insights into outages and meter telemetry.

Olameter Inc.

Taieb Lamine Ben Cheikh

Data Scientist