Added Pranay's updates.

Author: madeline-underwood

content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/cluster-monitoring.md

@@ -12,9 +12,13 @@ layout: learningpathall
 
 * Grafana is a visualization and analytics tool. It integrates with data sources from Prometheus to create interactive dashboards to monitor and analyze Kubernetes metrics. 
 
+{{% notice Note %}}
+The terrafom script executed in the previous step automatically installs Prometheus and Grafana in the EKS cluster. However, if you wish to have more flexibility with the installable versions for both, follow the instructions below.
+{{% /notice %}}
+
 ## Install Prometheus on your EKS cluster
 
-You can use Helm to install prometheus on the Kubernetes cluster. 
+You can use Helm to install Prometheus on the Kubernetes cluster. 
 
 Follow the [Helm documentation](https://helm.sh/docs/intro/install/) to install it on your computer.
 
@@ -105,7 +109,7 @@ kubectl get pods -n grafana
 
 Log in to the Grafana dashboard using the LoadBalancer IP and click on **Dashboards** in the left navigation page. 
 
-Locate a `Kubernetes/Compute Resources/Node` dashboard and click on it. 
+Locate a `Kubernetes/Compute Resources/Node (Pods)` dashboard and click on it. 
 
 You should see a dashboard like below for your Kubernetes cluster:
 

content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/elasticsearch-and-kibana.md

@@ -1,6 +1,6 @@
 ---
 title: Monitoring sentiment with Elasticsearch and Kibana
-weight: 4
+weight: 3
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
@@ -65,12 +65,12 @@ docker compose up
 ```
 If you do not have the `docker compose` plugin already installed, you can install it through the following commands:
 
-```Note
+{{% notice Note %}}
 sudo apt-get update
 sudo apt-get install docker-compose-plugin
-```
+{{% /notice %}}
 
-After the dashboard is set up, use the public IP of your server on port 5601 to access the Kibana dashboard. See Figure 2.
+After the dashboard is set up, use the public IP of your server on port `5601` to access the Kibana dashboard. See Figure 2.
 
 ![kibana #center](_images/kibana.png "Figure 2: Kibana Dashboard Setup.")
 

content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/sentiment-analysis.md

@@ -1,6 +1,6 @@
 ---
 title: Set up Sentiment Analysis with Amazon EKS
-weight: 3
+weight: 4
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
@@ -147,9 +147,9 @@ The following commands run the application with two executors, each with 12 core
  Before executing the `spark-submit` command, set the following variables:
 
 ```console
-export MASTER_ADDRESS=<K8S_MASTER_ADDRESS>
+export K8S_API_SERVER_ADDRESS=<K8S_API_SERVER_ENDPOINT
 export ES_ADDRESS=<IP_ADDRESS_OF_ELASTICS_SEARCH>
-export CHECKPOINT_BUCKET=<BUCKET_NAME>
+export CHECKPOINT_BUCKET=<S3_BUCKET_NAME>
 export ECR_ADDRESS=<ECR_REGISTERY_ADDRESS>
 ```
 
@@ -158,14 +158,14 @@ Execute the `spark-submit` command:
 ```console
 bin/spark-submit \
       --class bigdata.SentimentAnalysis \
-      --master k8s://$MASTER_ADDRESS:443 \
+      --master k8s://K8S_API_SERVER_ADDRESS:443 \
       --deploy-mode cluster \
       --conf spark.executor.instances=2 \
       --conf spark.kubernetes.container.image=$ECR_ADDRESS \
       --conf spark.kubernetes.driver.pod.name="spark-twitter" \
       --conf spark.kubernetes.namespace=default \
       --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
-      --conf spark.driver.extraJavaOptions="-DES_NODES=4$ES_ADDRESS -DCHECKPOINT_LOCATION=s3a://$CHECKPOINT_BUCKET/checkpoints/" \
+      --conf spark.driver.extraJavaOptions="-DES_NODES=$ES_ADDRESS -DCHECKPOINT_LOCATION=s3a://$CHECKPOINT_BUCKET/checkpoints/" \
       --conf spark.executor.extraJavaOptions="-DES_NODES=$ES_ADDRESS -DCHECKPOINT_LOCATION=s3a://$CHECKPOINT_BUCKET/checkpoints/" \
       --conf spark.executor.cores=12 \
       --conf spark.driver.cores=12 \
@@ -187,14 +187,19 @@ spark-twitter                               1/1     Running   0          12m
 
 ## X Sentiment Analysis
 
-Create a twitter(X) [developer account](https://developer.x.com/en/docs/x-api/getting-started/getting-access-to-the-x-api) and create a `bearer token`. 
+Create a twitter(X) [developer account](https://developer.x.com/en/docs/x-api/getting-started/getting-access-to-the-x-api) and download the `bearer token`. 
 
-Use the following commands to set the token and fetch the posts:
+Use the following commands to set the bearer token and fetch the posts:
 
 ```console
 export BEARER_TOKEN=<BEARER_TOKEN_FROM_X>
 python3 scripts/xapi_tweets.py
 ```
+{{% notice Note %}}
+You might need to install the following python packages, if you run into any dependency issues:
+* pip3 install requests.
+* pip3 install boto3.
+{{% /notice %}}
 
 You can modify the script `xapi_tweets.py` and use your own keywords. 
 
@@ -204,3 +209,23 @@ Here is the code which includes some sample keywords:
 query_params = {'query': "(#onArm OR @Arm OR #Arm OR #GenAI) -is:retweet lang:en",
                 'tweet.fields': 'lang'}
 ```
+
+Use the following command to send these processed tweets to Elasticsearch
+
+```console
+python3 csv_to_kinesis.py
+```
+
+Navigate to the Kibana dashboard using the following URL and analyze the tweets:
+
+```console
+http://<IP_Address_of_ES_and_Kibana>:5601
+```
+
+## Environment Clean-up
+
+Following this Learning Path will deploy many artifacts in your cloud account. Remember to destroy the resources after you've finished executing it. Use the following command to cleanup the resources:
+
+```console
+terraform destroy
+```