Published Release 1.4.0 (commando) of onesait Cloud Platform


On July 9, 2019, version 1.4.0-commando of the onesait Cloud Platform (also known as commando version, following our version policy: Version Policy onesait Cloud Platform) has been published.

You can follow our roadmap in Roadmap onesait Cloud Platform .

Below you can see the main features of this release:


This version supports the development of applications and microservices, for which the platform offers a centralized web console that supports the administration, configuration and development of all types of applications, integrating the creation of applications, its deployment on the CaaS infrastructure, apificación , visualization, ...

In this release we have worked on the following features:

  • Complete support Relational Databases: this allows to connect the platform to an existing database (Oracle, Postgresql, MySQL, SQL Server, HIVE, ...) and generate the ontologies that represent the different tables. Once we have done this, we will be able to work normally with the rest of the components, aplying queries about these ontologies or making complete dashboards.

These posts show how an administrator can create the connection: How to create a JDBC connection to a relational database and how a developer can connect to this database, generate an ontology and from there a dashboard Creating an ontology from a relational database and showing it in a dashboard.

  • Drools Integration as a Business Rules Engine: with this integration now business users can develop business rules from the platform's own Control Panel, these can be triggered either by the arrival of an instance of an ontology or be directly invoked via Your REST API:

    This functionality is open to any Developer, in this tutorial (Rules Engine) (ES) Cómo crear reglas de negocio it is explained how to create and execute the rules.

  • Full support for the creation of Time Series models: the functionality that allows managing Time Series structures in the platform has been improved, in a transparent way the underlying repository, allowing now to use Mongo, Kudu or Elasticsearch for this modeling. The functionality is described here Soporte para modelado TimeSeries en Ontologías

  • Orchestration and invocation of APIS from Flow Engine: this functionality allows using the FlowEngine engine to invoke and orchestrate APIS available in the platform. So I can visually create complex orchestration flows at the entrance of an invocation, like this shopping cart example:

In these 2 tutorials you can see how (FlowEngine) (ES) Invocación de APIs desde el FlowEngine y (FlowEngine) (ES) Cómo orquestar APIs desde el FlowEngine.

  • Filters in Dashboards Engine: this functionality allows you to associate filters to the attributes that make up the datasource of a gadget, so that over the gadget we can select how to filter the data. These filters can also be dragged to other gadgets:

This example shows all the possibilities of the filters: Cómo usar Filtros en los Dashboards?

  • Javascript API of the Dashboard Engine: this API allows us to instantiate the gadgets and dashboards of the platform from any Javascript application using the Javascript API of the dashboards:

Here is how to use the API API Javascript del Dashboard Engine

  • Theme style in Control Panel: this functionality allows a platform administrator to configure the look of the Platform Control Panel, so that it is more integrated with the corporate look:

More information here:: /wiki/spaces/OP/pages/142835838

  • Organization of platform elements in tree mode: this functionality allows a user to create their own organizational structures, for example to join the dashboards of a client type. These categorizations can be shared with other users:

More information here: (ES) ¿Cómo crear un árbol de categorización?

  • Full support of GeoJSON geometries in Ontologies: until this version was the developer himself who had to expand the schema of an ontology if he wanted his geometry to store polygons, for example. With this functionality from the guided creation you can select any GeoJSON geometry:

This guide describes the operation: Geometries support in ontologies

  • New API and Flow Engine component for sending emails: incorporated as a transversal service any user of the platform can make use of this API and component for sending emails. The administrator can configure the SMTP server from which the emails are sent:

More information: Mail Service & Mail FlowEngine Component 

In addition, an example has been developed that shows how to send reports generated from the Report Engine component of the platform via the mail sending component: How to create an API REST to send e-mails with FlowEngine?


This version of the platform is focused on the creation of IoT systems in which Edge and Cloud capabilities of the platform can collaborate, allowing bi-directional communication with devices, modeling and deployment of Digital Twins, ...

In this quarter we have focused the work in these lines:

  • Synoptic development support: this synoptics functionality is very useful in industrial scenarios where you need to be monitoring and acting on physical signals. The synoptics allow you to create SCADA-type representations in the same dashbaord engine:

The functionality allows uniting SCADA-like representations with all kinds of gadgets and interacting with each other.

A complete example: (Dashboards) (ES) Soporte Sinópticos en motor de Dashboards


This version of the platform focuses on supporting the development of systems and applications that need to use the Big Data, AI or distributed ingest capabilities of the platform.

In this quarter we have focused on:

  • Incorporation of Intelligence Services in the platform: the platform now offers APIS focused on:
    • Language: text analysis, entities, feeling, ...
    • Translation of texts
    • Analysis of images including OCR, detection of entities, ...
    • Speech: enabling TextToSpeech for example

These services are described in this guide: Servicios API REST Onesait Platform Intelligence

You can also check the different examples:

Example call to Language: sentiment service

Example call to Translation: translation service

Example call to Vision: ocr service

  • Import from Notebooks developed in Jupyter: our Notebook engine is based on the Apache Zeppelin technology, chosen for its modular approach (interpreters) and visual capabilities. Currently one of the most used notebook engines is Jupyter (Especially for Python users). A utility has been created that allows any notebook developed in Jupyter to be imported automatically. See guide: (Notebooks) (ES) Cómo importar notebooks Jupyter

  • API Restart interpreters Notebooks: this REST API allows to restart the interpreter before or after the execution of an algorithm, in this way I can free memory or control the use of resources. A user only has permission to restart the interpreters of their notebook instances: (Notebooks) (ES) Cómo reiniciar mi intérprete