1. Enforcing IP-Based Security Policies in Databricks


    Enforcing IP-based security policies in Databricks can typically involve configuring network security settings to whitelist certain IP addresses, thereby restricting access to Databricks workspaces and enforcing network security. To achieve this in a Pulumi program, we might need to work with security groups or network configuration that controls inbound and outbound traffic to Databricks.

    However, as of my knowledge cutoff in early 2023, Databricks itself may not be directly supported in Pulumi for setting IP-based security policies. In practice, you would likely use a combination of Databricks network controls within the Databricks console and the cloud provider's specific security features such as AWS Security Groups, Azure Network Security Groups, or GCP Firewall rules.

    When using Pulumi to manage Databricks, you can create and manage resources such as Databricks Workspaces, Clusters, Jobs, Tables, and Permissions. For security policies specifically, you'd usually work within the constraints of the cloud provider that your Databricks instance is hosted on.

    Let's consider a scenario where your Databricks instance is hosted on AWS. You would typically use Pulumi to set up an AWS Security Group that allows traffic only from certain IP addresses and associate it with the resources used by Databricks.

    Below is an example Pulumi program that demonstrates how to create an AWS Security Group with an IP-based rule and associate it with an EC2 instance. It's a starting point to show how you might manage network security, not directly applicable to Databricks but illustrative of the concept.

    import pulumi import pulumi_aws as aws # Create an AWS security group to enforce the IP-based policy ip_based_security_group = aws.ec2.SecurityGroup('ip-allow-sg', description='Allow access from specific IP addresses', ingress=[ # Replace 'x.x.x.x/32' with the IP range you want to whitelist aws.ec2.SecurityGroupIngressArgs( from_port=0, # Adjust the port number as necessary to_port=65535, # Adjust the port range as necessary protocol='tcp', # Can be 'tcp', 'udp', or 'icmp' cidr_blocks=['x.x.x.x/32'], # IP ranges to whitelist ), ], # For demonstration, this security group allows all outbound traffic. # Tailor the outbound rules to your specific requirements. egress=[ aws.ec2.SecurityGroupEgressArgs( from_port=0, to_port=0, protocol='-1', # '-1' Allows all traffic cidr_blocks=[''], ), ] ) # Example case: Using the Security Group with an EC2 instance # In practice, you would associate this with the resources used by Databricks. example_instance = aws.ec2.Instance("example-instance", instance_type="t2.micro", security_groups=[ip_based_security_group.name], # Attach the security group here ami="ami-0c55b159cbfafe1f0" # Example AMI, use the correct one for your region & OS ) # Output the security group ID pulumi.export('security_group_id', ip_based_security_group.id)

    In the above example, we created a security group named ip-allow-sg that has inbound rules to allow traffic from a specific IP address that you would define in place of x.x.x.x/32. The ingress specifies the allowed incoming traffic, while the egress specifies the allowed outgoing traffic from the instance. We then created an EC2 instance and associated it with the security group.

    To apply IP-based security policies to Databricks, you would need to adjust the resources and possibly use Databricks networking features or those provided by your cloud provider, while maintaining a similar concept as described above. Always ensure that you're following best practices for your cloud provider and the requirements of your own security policies.