Predicting and preventing equipment failures is critically important to utilities. Olameter, a leader in utility asset management and network communications, understands this well. They assist electric, water, gas, and telecommunications companies, as well as energy retailers, in maintaining smooth operations. However, managing the vast amounts of data required for predictive maintenance poses a considerable challenge.
Olameter faced a massive data problem. They needed to process large volumes of XML data from electricity meters and transformers to map out their network and predict outages. Their initial approach using custom .NET applications was slow and inefficient. How slow? It took over six months to process just one year’s worth of data. This inefficiency left Olameter with a decade’s backlog of unprocessed data – a significant obstacle to progress.
To tackle this data challenge, Olameter partnered with Onehouse. Within just two weeks, Onehouse developed a custom XML ingestion solution. This new solution can intelligently parse the XML data, making it ready for analysis and allowing Olameter to run SQL queries against the parsed data through a user-friendly interface. Powered by Apache Hudi, Onehouse enables Olameter to process data incrementally, efficiently managing both the backlog and new, incoming data.
The collaboration hasn’t stopped there. Onehouse assists Olameter in flattening deeply nested data structures and exploding large arrays, optimizing the data for faster querying and analysis. By using geo-spatial clustering, they make the data more accessible and useful, reducing months of processing to just days.
Onehouse’s support extends beyond data processing. Today, the two companies work together to build robust downstream pipelines and machine learning (ML) models, providing valuable operational insights and improving service reliability for Olameter’s clients. This ongoing collaboration ensures that Olameter can stay ahead with advanced data solutions.
Thanks to this partnership, Olameter reduced data processing times from years to days and achieved near real-time XML ingestion. This seamless scaling and reduction in infrastructure management hassles and costs allows Olameter to build out and leverage predictive maintenance capabilities, enhancing quality of service and customer satisfaction.
Interested in how Olameter continues to overcome their data challenges? Read the full case study to discover the detailed journey and technical solutions that set a new standard in data processing and analytics for the utilities sector.
Be the first to read new posts