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 Enjoy your Spark journey!
 
+For more information, please consult the [User Guide](docs/user_guide.md).
+
+[
+
 
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+# User Guide: Accessing Spark Jupyter Notebook
+
+## Prerequisites
+
+### SSH Access:
+   * Ensure you have SSH access to the remote server (`login1.berkeley.kbase.us`).
+   * If you do not have access, please contact the KBase System Admin team.
+
+## Step-by-Step Guide
+
+### 1. Create SSH Tunnel:
+
+Execute the following command to create an SSH tunnel to the remote server (`login1.berkeley.kbase.us`):
+
+```bash
+ssh -f -N -L localhost:44042:10.58.2.201:4042 <kbase_developer_username>@login1.berkeley.kbase.us
+```
+   
+* `-f`: Run SSH command in the background.
+* `-N`: Do not execute a remote command.
+* `-L localhost:44041:10.58.2.201:4041`: Forward port `44041` on your local machine to port `4041` on the remote machine `10.58.2.201`.
+* `<kbase_developer_username>`: Your username for SSH access. Contact the KBase System Admin team if you do not have access.
+* `@login1.berkeley.kbase.us`: The remote SSH server.   
+   
+### 2. Access Jupyter Notebook:
+   
+Open a web browser and navigate to the following URL:
+
+```
+http://localhost:44041
+```
+This will open the Jupyter Notebook interface running on the remote server.
+
+### 3. Working in the Shared Jupiter Notebook Environment:
+
+Since this is a shared environment, it’s important to create your own directory and keep your work organized.
+
+![user_folders](screen_shots/user_folders.png)
+
+
+We have provided a collection of example notebooks to help you get started. You can find them in the `examples` directory.
+
+### 4. Using Pre-loaded Functions:
+
+To make your development easier, several helper functions and extensions are preloaded in the Jupyter environment.
+
+#### 4.1. Creating a Spark Session:
+
+Use the `get_spark_session` function to create or get a Spark session. 
+
+This function will automatically set up the Spark session with the correct configurations, including setting the 
+master URL and other necessary Spark configurations. By using this function, you ensure that your Spark session is 
+properly configured to interact with the cluster.
+
+##### Example Usage:
+```python
+spark = get_spark_session()
+```
+
+#### 4.2. Displaying DataFrames:
+Use the `display_df` function to display pandas or Spark DataFrames interactively.
+
+The `display_df` function is designed to provide an interactive tabular display of DataFrames within Jupyter Notebooks. 
+It leverages the [itables](https://github.com/mwouts/itables) extension, which enhances the display of data tables by providing features like sorting, 
+filtering, and pagination directly in the notebook interface.
+
+##### Example Usage:
+```python
+display_df(spark.sql(f"SELECT * FROM {namespace}.annotation"))
+```
+![display_df](screen_shots/display_func.png)
+
+### 5. Accessing Data:
+
+#### 5.1. Showing Available Namespaces and Listing Tables:
+To list all namespaces (databases) and display the tables within each namespace, you can use the following code snippet:
+
+```python
+namespaces = spark.sql("SHOW DATABASES").collect()
+
+# List tables within each namespace
+for namespace in namespaces:
+    namespace_name = namespace.namespace
+    print(f"Tables in namespace: {namespace_name}")
+    spark.sql(f"SHOW TABLES IN {namespace_name}").show(50, truncate=False)
+```
+
+### 6. Closing the Spark Session:
+Please remember to close the Spark session when you are done with your work. This will release the resources and 
+prevent any memory leaks.
+
+```python
+spark.stop()
+```
+
+Please be aware that your session will automatically close after `1 hour`. Should you require an extension, simply invoke 
+`get_spark_session()` to initiate a new session.
+
+### 7. Common Issues and Troubleshooting:
+
+#### 7.1. Resource Issues:
+
+##### Error Message:
+```python
+24/06/16 00:55:42 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
+```
+
+This warning indicates that the Spark job could not acquire the necessary resources to start execution. Please contact
+the CDM team for assistance in resolving this issue.
+
+#### 7.2 SparkMonitor Issues:
+
+##### Error Message:
+```python
+Exception in thread Thread-5:
+Traceback (most recent call last):
+  File "/opt/bitnami/python/lib/python3.11/threading.py", line 1045, in _bootstrap_inner
+    self.run()
+  File "/opt/bitnami/python/lib/python3.11/site-packages/sparkmonitor/kernelextension.py", line 126, in run
+    self.onrecv(msg)
+  File "/opt/bitnami/python/lib/python3.11/site-packages/sparkmonitor/kernelextension.py", line 143, in onrecv
+    sendToFrontEnd({
+  File "/opt/bitnami/python/lib/python3.11/site-packages/sparkmonitor/kernelextension.py", line 223, in sendToFrontEnd
+    monitor.send(msg)
+  File "/opt/bitnami/python/lib/python3.11/site-packages/sparkmonitor/kernelextension.py", line 57, in send
+    self.comm.send(msg)
+    ^^^^^^^^^
+AttributeError: 'ScalaMonitor' object has no attribute 'comm'
+```
+
+This error occurs when there is an issue with the SparkMonitor kernel extension, specifically related to the 
+communication between the kernel and the front end. The error indicates that the comm attribute is missing from the 
+ScalaMonitor object.
+
+##### Solution:
+
+Restart the Jupyter kernel associated with your notebook to reset the SparkMonitor extension. 
+
+* Click on the "Kernel" menu and select "Restart Kernel" to restart the kernel
+![restart_kernel](screen_shots/restart_kernel.png)
+
+If the issue persists after restarting the kernel, please contact the CDM team for further assistance.
+