Suricate - Analytics as a Service

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Analytics as a Service



  • allows you to upload or stream data so the Service can aggregate it.
  • allows a Data Scientist to perform Analytics and visualize the results.
  • supports the processing and acting (trigger actions) on the analysis and thereby the creation of continuously adjusted agile Systems & Strategies based on up-to-date insights.

Concepts of a Analytics as a Service

The four main concepts are:


Supplying user defined data: Stream data into the service or upload the data in an internal or external Storage (Object, Relational, etc). The data can then be aggregated, pre-processed and cached.


Supplying user defined logic: Use Python interactive scripting capabilities to perform analytics and visualize the results. The Data Scientist can interact with the Service via a Web UI or REST API.


Uses Cloud Computing features to perform the computational aspect of the analytics.


Process the learned models and trigger actions on insights gained, to create agile Systems & Strategies.


  • Provides interactive Python notebooks to allow the definition of user-defined logic for analytics and processing of data
    • Supports many existing packages for visualization and analytics like Pandas, scikit-learn, NumPy, ...
  • Offers an RESTful API to support machine based interactions to trigger the analytics and processing.
  • Provides hooks to interact with external system (Object Storage & the Systems which needs agility enhancements)
  • Easy to deploy (as WSGI application) into existing private and public clouds.
  • (Optional) Integration with Hadoop and IaaS based service to enhance the computational aspects.


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