.. _overview::

Overview
========

The ENES Climate Analytics Service (ECAS) will enable scientific end-users to perform data analysis experiments on large volumes of climate data, by exploiting a PID-enabled, server-side, and parallel approach.

It aims at providing a paradigm shift for the ENES community with a strong focus on data intensive analysis, provenance management, and server-side approaches as opposed to the current ones mostly client-based, sequential and with limited/missing end-to-end analytics workflow/provenance capabilities.

To login into Jupyter, enter the username and password provided to you after registration at DKRZ. If you would like an account or need assistance, please email ecas-support.

Useful links
------------

* ECASLab at DKRZ https://ecaslab.dkrz.de/jupyter/
* Registration https://ecaslab.dkrz.de/registerproc.html
* Ophidia documentation http://ophidia.cmcc.it/documentation/index.html

What you can do with ECAS?
--------------------------

* Data analysis
* Visualization
* Interactive programming

Who is providing ECAS?
----------------------

ECAS is being developed and deployed at DKRZ and CMCC. We are trying to keep both instances synchronized as much as possible.
However, in some cases, usage of the service can slightly differ between the two instances. 
More or less steps might be required from the users.
This documentation is principally for ECASLab at DKRZ. 
For specific information about Ophidia framework, we suggest that you use the official documentation. 
 

ECAS Users
----------

* Climate community