To remediate high disk usage in AWS Redshift clusters using AWS CLI, follow these steps:

1. Check Disk Usage: First, you need to check the disk usage of your Redshift cluster to identify the extent of the issue. You can use the following AWS CLI command to describe the cluster and get information about its disk usage:

   aws redshift describe-clusters --cluster-identifier YOUR_CLUSTER_IDENTIFIER
   

   Replace YOUR_CLUSTER_IDENTIFIER with the actual identifier of your Redshift cluster.

2. Scale Cluster: If the disk usage is high, you can scale your Redshift cluster by modifying the cluster to increase the number of nodes or node type. To do this, use the following AWS CLI command:

   aws redshift modify-cluster --cluster-identifier YOUR_CLUSTER_IDENTIFIER --node-type NEW_NODE_TYPE --number-of-nodes NEW_NUMBER_OF_NODES
   

   Replace YOUR_CLUSTER_IDENTIFIER with the actual identifier of your Redshift cluster, NEW_NODE_TYPE with the new node type you want to use, and NEW_NUMBER_OF_NODES with the desired number of nodes.

3. Monitor Cluster: After scaling the cluster, monitor the disk usage to ensure that it has decreased to a manageable level. You can use the describe-clusters command mentioned in step 1 to periodically check the disk usage.

By following these steps, you can effectively remediate high disk usage in AWS Redshift clusters using AWS CLI.

To remediate high disk usage in AWS Redshift clusters using Python, you can automate the process by periodically checking the disk usage metrics and scaling the cluster up if the disk usage exceeds a certain threshold. Here are the step-by-step instructions to remediate this issue:

1. Install Boto3: Boto3 is the AWS SDK for Python, which allows you to interact with AWS services. You can install it using pip:

   pip install boto3
   

2. Create a Python script: Create a Python script that will check the disk usage of the Redshift cluster and scale it up if the disk usage is above the threshold. Below is a sample script to get you started:

   import boto3
   
   # Initialize the Redshift client
   redshift = boto3.client('redshift')
   
   # Define the cluster identifier
   cluster_identifier = 'your-redshift-cluster-id'
   
   # Get the disk usage metrics for the cluster
   response = redshift.describe_cluster_performance(clusterIdentifier=cluster_identifier)
   
   # Check the disk space usage
   disk_space_usage = response['ClusterPerformance']['DiskSpaceUsage']
   disk_space_threshold = 80  # Define your own threshold here
   
   if disk_space_usage > disk_space_threshold:
       # Scale up the cluster
       redshift.modify_cluster(ClusterIdentifier=cluster_identifier, NumberOfNodes=2)  # Increase the number of nodes as needed
       print(f"Cluster {cluster_identifier} scaled up due to high disk usage.")
   else:
       print(f"Disk usage for cluster {cluster_identifier} is within the threshold.")
   
   

3. Set up AWS credentials: Make sure you have set up your AWS credentials either by exporting them as environment variables or using AWS CLI aws configure command.

4. Schedule the script: You can schedule the script to run periodically using cron jobs or AWS CloudWatch Events to continuously monitor the disk usage of the Redshift cluster and scale it up when needed.

5. Monitor the remediation: After implementing the script, monitor the disk usage of the Redshift cluster regularly to ensure that it stays within the threshold and adjust the threshold or scaling logic if necessary.

By following these steps, you can automate the remediation of high disk usage in AWS Redshift clusters using Python.