adding dodas documentation (#425)

* adding dodas documentation

content/en/users/compute/orchestration/_index.md

@@ -26,11 +26,9 @@ levels of abstractions and features. See below to understand which to choose
 | [Infrastructure Manager](im)                 | VMs, containers, storage  | Used to run workloads on a single IaaS Cloud provider. |
 | [Elastic Cloud Compute Cluster](ec3)         | VMs, containers, storage  | Used when you need to run workloads on clusters that can be elastically scaled and potentially span more than one IaaS Cloud provider. |
 | [Workload Manager](workload-manager)         | VMs, containers, HTC jobs | Used to efficiently distribute, manage, and monitor computing workloads. |
+| [Dynamic On-Demand Analysis Service](dodas)  | Containers, storage (caches) | Used when you need to process your data either interactively or via a batch system. |
 <!--
 | [PaaS Orchestrator](indigo-paas)             | VMs, containers, HTC jobs | Used when you have both IaaS Cloud and HTC workloads. The DEEP Platform uses it for ML/DL workloads. |
-| [Dynamic On-Demand Analysis Software](dodas) | Containers                | Used when your workload is composed of Docker containers (Helm charts). |
--->
-
 <!-- markdownlint-enable line-length -->
 
 The following sections offer more details about each of these orchestrator tools.

content/en/users/compute/orchestration/dodas/_index.md

@@ -1,13 +1,38 @@
 ---
 title: Dynamic On-Demand Analysis Software
 linkTitle: Dynamic On-Demand Analysis Software
-draft: true
 type: docs
 weight: 40
 description: >
-  Subtitle here
+  Instantiate on-demand customizable infrastructures for data analysis
 ---
 
 ## What is it?
 
-TBD
+[Dynamic On-Demand Analysis Software (DODAS)](https://dodas-iam.cloud.cnaf.infn.it/login)
+enables the execution of user analysis code both in batch mode and
+interactively via the Jupyter interface. DODAS is highly customizable and offers
+several building blocks that can be combined together in order to create the
+best service composition for a given use case. The currently available blocks
+allow to combine Jupyter and HTCondor as well as Jupyter and Spark or simply a
+jupyter interface. In addition, they allow the management of data via caches to
+optimize the processing of remote data. This can be done either via XCache or
+MinIO S3 object storage capabilities. DODAS is based on docker containers and
+the related orchestration relies on Kubernetes that enables the possibility to
+compose the building blocks via a web-based user interface thanks to Kubeapps.
+
+In order to deploy it services over various backend, DODAS relies on
+[Infrastructure Manager](../im) and
+[INDIGO PaaS](https://indigo-paas.cloud.ba.infn.it/home/login)
+
+{{% alert title="Tip" color="info" %}} If you want to give it a try and deploy
+your cluster in the EGI Federation:
+[DODAS](https://dodas-iam.cloud.cnaf.infn.it/login). {{% /alert %}}
+
+{{% alert title="Note" color="info" %}} For detailed information about DODAS
+please see its [documentation](https://web.infn.it/dodas/index.php/en/). It was
+also presented in one of the [EGI Webinars](https://www.egi.eu/webinars/), more
+details are available on the [indico page](https://indico.egi.eu/event/5695/)
+and the video recording is available on
+[YouTube](https://www.youtube.com/watch?v=bcURl4ESRW8&ab_channel=EGI).
+{{% /alert %}}