docs(dwc): add doc on persistent volume MTA-5885

Author: SamyOubouaziz

pages/data-lab/concepts.mdx

@@ -12,6 +12,10 @@ categories:
   - managed-services
 ---
 
+## Apache Spark Cluster 
+
+An Apache Spark cluster is an orchestrated set of machines over which the distributed/Big data calculus is going to be processed. In the case of this project, the Apache Spark cluster is a Kubernetes cluster, upon which Apache Spark has been installed in every pod deployed. For more details, check out the [Apache Spark documentation](https://spark.apache.org/documentation.html).
+
 ## Data Lab
 
 A Data Lab is a project setup that combines a Notebook and an Apache Spark Cluster for data analysis and experimentation. it comes with the required infrastructure and tools to allow data scientists, analysts, and researchers to explore data, create models, and gain insights.
@@ -40,14 +44,15 @@ Lighter is a technology that enables SparkMagic commands to be readable and exec
 
 A notebook for an Apache Spark cluster is an interactive, web-based tool that allows users to write and execute code, visualize data, and share results in a collaborative environment. It connects to an Apache Spark cluster to run large-scale data processing tasks directly from the notebook interface, making it easier to develop and test data workflows.
 
-## Apache Spark Cluster 
+## Persistent volume
 
-An Apache Spark cluster is an orchestrated set of machines over which the distributed/Big data calculus is going to be processed. In the case of this project, the Apache Spark cluster is a Kubernetes cluster, upon which Apache Spark has been installed in every pod deployed. For more details, check out the [Apache Spark documentation](https://spark.apache.org/documentation.html).
+A Persistent Volume (PV) is a cluster-wide storage resource that ensures data persistence beyond the lifecycle of individual pods. Persistent volumes abstract the underlying storage details, allowing administrators to use various storage solutions.
 
 ## SparkMagic
 
 SparkMagic is a set of tools that allows you to interact with Apache Spark clusters through Jupyter notebooks. It provides magic commands for running Spark jobs, querying data, and managing Spark sessions directly within the notebook interface, facilitating seamless integration and execution of Spark tasks. For more details, check out the [SparkMagic repository](https://github.com/jupyter-incubator/sparkmagic).
 
 
 ## Transaction
+
 An SQL transaction is a sequence of one or more SQL operations (such as queries, inserts, updates, or deletions) executed as a single unit of work. These transactions ensure data integrity and consistency, following the ACID properties: Atomicity, Consistency, Isolation, and Durability, meaning all operations within a transaction either complete successfully or none of them take effect. An SQL transaction can be rolled back in case of an error.

pages/data-lab/how-to/create-data-lab.mdx

@@ -34,7 +34,10 @@ Data Lab for Apache Spark™ is a product designed to assist data scientists and
       <Message type="note">
       Provisioning zero worker nodes lets you retain and access you cluster and notebook configurations, but will not allow you to run calculations.
       </Message>
-    - Optionally, choose an Object Storage bucket in the desired region to store the data source and results.
+    - Activate the [persistent volume](/data-lab/concepts/#persistent-volume) if required, then enter a volume size according to your needs.
+      <Message type="note">
+      Persistent volume usage depends on your workload, and only the actual usage will be billed, within the limit defined. A minimum of 1 GB is required to run the notebook.
+      </Message>
     - Enter a name for your Data Lab.
     - Optionally, add a description and/or tags for your Data Lab.
     - Verify the estimated cost.