Remote health monitoring (RHM) systems collect data from sensors on mining equipment in use across the globe to analyze productivity, availability, utilization, and status. Issues with legacy RHM systems include low performance, low scalability in meeting growing equipment implementations and data volumes, availability and manageability issues (up to 10 hours of down time), and escalating costs to maintain the system. Using an open source technology stack and distributed processing, we implemented a solution that delivers high performance, low latency results – achieving over 120,000 writes per second sustained.
We will share the technical architecture and tools we used to implement the solution:
Some of the challenges encountered in the project included collecting data across different geographies, processing the data in real-time, applying business rules, and providing remote monitoring. We will also cover technical benchmarks achieved for streaming, distribution, in-memory computation, data syncing into Cassandra, and visualization as well as some tips for easy integration.
Meet us at Booth #105 and Checkout our Open Source ETL on Hadoop Utility developed in partnership with Capital One..
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