Data integration as-fast-as possible

Maximum performance with data integration of time series

How we were able to reduce the data footprint of a condition monitoring system by a factor of 200 to not only increase performance but also ensure scalability.

1. starting position

  • The task was to replace a legacy system for monitoring the condition of rolling bearings that was already operating beyond the limits of its scalability.
  • Performance and memory problems were exacerbated by the addition of further monitoring objects.
  • A data inventory of currently 3 years and the continuous expansion of monitoring data did not allow the old system to be taken offline during the migration.
  • The new system had to be able to process both existing data records (>1Tbyte) and new incoming data.
  • In addition, the new system was to be prepared for extended data processing as part of predictive maintenance.

2. procedure/solution approach

  • Various available open source tools were evaluated in terms of stability, reliability and performance.

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Contact person

Werner Spiegl

Head of Consulting

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