Governance over analytics platform on IoT data

Control your analytics at scale on large amount of IoT data generated every day

Key Information


IoT

Every mining machine carries more than 10 sensors

1,200

Tasks executed every hour

Big Data

Integration with various Big Data tools

Initial Needs

Komatsu acquired JoyGlobal in 2017
Industry: Mining

Komatsu produces mining equipments which embed multiple sensors that helps users in their daily tasks. It can help them prevent issues, add preventive maintenance, target areas with higher production yield or even target training for users.

All the information collected required to be stored and analyse to provide value.

Activeeon roles is then to orchestrate and schedule analytic workflows at scale and on any cloud.

Some of the requirements where:

  • Composable and flexible workflows to orchestrate data analytics
  • REST interface for full integration
  • Powerful searchable interface by status, groups, machines and execution times

Architecture

The final architecture can be seen below.

Big Data and IT architecture for data analytics

As shown above the data is ingested and stored in different storage types. ProActive from Activeeon is consuming this data based on parameters collected from the master data storage to perform analytics. The results are then published in multiple place to be consumed by operators.

Integration

Activeeon has achieved this integration thanks to its open architecture. ProActive has managed to be integrated with multiple Big Data solutions such as:

  • Spark
  • Hadoop, Cloudera
  • HBase
  • MEMSQL

Moreover, the various languages supported by the solution enabled the data analytics team to select their favorite language such as R, Python or Matlab.