A look at the Notebooks

This module provides data scientists with a multi-user web environment where they can develop analysis models ob the information stored on the Platform interactivelyusing their favorite languages (Python, Spark, R, Tensorflow, Keras, SQL, ...).

Notebooks are defined and managed from the platform's own Control panel, from where we can:

List them, depending on the role and permissions, you will see those belonging to you, and you can assign permissions, ...

Create, edit and run the notebooks from the Control Panel itself:

Publish a Notebook as a microservice, in which case the platform automatically deploys that microservice and APIfies it so that it can be accessed according to the security identified on the platform.
The component is supported on the open-source Apache Zeppelin software (https://zeppelin.apache.org) on which a platform interpreter has been created:

The module allows you to create algorithms in different languages based on the needs and preferences of the data scientist, supporting among others Python, Spark, R, SparkSQL, SQL, SQL HIVE, Shell, ...

It has the ability to combine code from several languages within the same notebook (using different interpreters in each code paragraph):

It allows the use of Spark, to upload files to HDFS, to load data into HIVE tables, to launch queries or to perform a complex Machine Learning (ML) process through the MLlib libraries of Spark, R or Python, ...
Example of loading data using a Spark interpreter::

Example of training and prediction of a predictive model using the Python interpreter's ML libraries:

It allows you to use advanced analytics libraries, both internal and external (downloaded from external or own repositories), such as Python NLTK or SparkNLP that allows you to program, for instance, the extraction of entities from a text, detection of feeling in said text, train models, etc., on the platform information.

These Notebooks can be orchestrated visually through the platform so that, depending on the result of one of them, another is invoked:

It allows to create visualizations of different types, even adding HTM code via Angular, which allows us to create sophisticated visualizations.
Example of visualization of data by means of graphs::

Example of geospatial data visualization: