Amazon MSK is a fully managed Apache Kafka service that makes it easy to build and run applications using Apache Kafka. Using Terraform to define the entire MSK environment transforms manual console work into repeatable infrastructure as code. Terraform can provision the VPC, subnets, security groups, IAM roles, the MSK cluster, and a client EC2 instance in a single set of configuration files. With a few simple commands you can create, update, or destroy the whole setup consistently across environments.
Understanding AWS MSK Deployment Options
AWS MSK offers two deployment types. One type requires you to manage cluster capacity by choosing instance types and the number of brokers. It offers more control over performance, scaling, and configuration, making it suitable for predictable workloads and production environments that need fine-tuning.
The other type is where AWS automatically manages capacity, scaling, and broker infrastructure. You do not need to choose instance types or manage brokers.
Kafka is a distributed streaming platform used for building real-time data pipelines and streaming apps. AWS MSK is a fully managed service that makes it easy to run Apache Kafka on AWS without managing the infrastructure yourself.
Core Benefits of Terraform for MSK Topic and Cluster Management
This approach provides several key benefits.
- Automation – Terraform automates the creation, modification, and deletion of MSK topics.
- Consistency and repeatability – Terraform configurations provide consistent topic structures and settings across your entire Amazon MSK environment. This simplifies management and reduces the likelihood of configuration drift.
- Scalability – Terraform enables you to provision and manage large numbers of MSK topics, facilitating the growth of your Amazon MSK environment.
- Version control – Terraform configurations are stored in version control systems, allowing you to track changes, roll back if needed, and collaborate effectively on your Amazon MSK infrastructure.
By using Terraform for MSK topic configuration management, you can streamline operations, minimize errors, and have a robust and scalable Amazon MSK environment.
Prerequisites and Environment Setup
Before proceeding with the solution, make sure you have the following resources and access.
- AWS Account: Ensure you have an AWS account with programmatic access and that your AWS credentials are configured locally using the AWS CLI.
aws configure - S3 Bucket: S3 Bucket for remote backend.
- Terraform Setup: Make sure Terraform is installed on your local environment.
Terraform is straightforward to set up.
Terraform is easy to install.
Additional prerequisites for a provisioned cluster setup include:
- AWS CLI configured with appropriate permissions
- Terraform installed version 1.0.0 or later
- Basic understanding of Apache Kafka
- VPC with private subnets
To simplify the setup, use the provided AWS CloudFormation template. This template will create the necessary Amazon MSK provisioned cluster and required resources.
Reference Architecture and Project Structure
A complete Amazon MSK infrastructure solution provisions a complete Amazon MSK infrastructure, including the VPC, subnets, security groups, IAM roles, the MSK cluster, and a client EC2 instance. With a small set of Terraform commands, you can reliably create, update, and decommission the entire environment in a consistent and repeatable manner across environments.
Resources Covered in This Guide:
- A VPC with public and private subnets across three Availability Zones.
- Full networking setup, including Internet Gateway and route tables.
- A secure Amazon MSK Serverless cluster.
- An EC2 instance configured with Kafka tools and authentication.
- IAM roles and security groups to enable secure communication between the EC2 instance and the MSK cluster.
What We'll Build:
- A new VPC with public subnets across three Availability Zones.
- All necessary networking, Internet Gateway, Route Tables.
- A secure MSK Kafka cluster.
- An EC2 instance pre-configured with Kafka tools and the correct authentication settings.
- IAM roles and security groups that allow the EC2 instance to securely communicate with the MSK cluster.
Project Structure
terraform-msk/
├── main.tf
├── variables.tf
├── outputs.tf
├── modules/
│ └── msk/
│ ├── main.tf
│ ├── variables.tf
│ └── outputs.tf
└── config/
└── server.properties
Networking, Security Groups, and IAM Foundations
The solution provisions a complete Amazon MSK infrastructure. Networking is established with a VPC with public and private subnets across three Availability Zones, full networking setup including Internet Gateway and route tables.
Security is enforced through security groups and IAM roles. The Terraform configuration defines client authentication with SASL IAM enabled and TLS with certificate authority ARNs. IAM roles and security groups enable secure communication between the EC2 instance and the MSK cluster.
It also covers configuring a remote Terraform backend using Amazon S3 to securely store and manage the Terraform state.
Terraform Configuration for Provisioned MSK Clusters
Create modules/msk/main.tf:
resource "aws_msk_cluster" "main" {
cluster_name = "${var.project_name}-cluster"
kafka_version = var.kafka_version
number_of_broker_nodes = var.number_of_broker_nodes
broker_node_group_info {
instance_type = var.instance_type
client_subnets = var.subnet_ids
security_groups = [aws_security_group.msk.id]
storage_info {
ebs_storage_info {
volume_size = var.volume_size
}
}
}
encryption_info {
encryption_at_rest_kms_key_arn = aws_kms_key.msk.arn
encryption_in_transit {
client_broker = "TLS"
in_cluster = true
}
}
configuration_info {
arn = aws_msk_configuration.main.arn
revision = aws_msk_configuration.main.latest_revision
}
client_authentication {
sasl {
iam = true
}
tls {
certificate_authority_arns = [aws_acmpca_certificate_authority.msk.arn]
}
}
logging_info {
broker_logs {
cloudwatch_logs {
enabled = true
log_group = aws_cloudwatch_log_group.msk.name
}
firehose {
enabled = true
delivery_stream
This guide demonstrates how to set up and manage MSK using Terraform. MSK clusters take 15-30 minutes to create.
Monitoring, Logging, and Durability Best Practices
Use IAM authentication over SCRAM when possible, enable Prometheus monitoring, and ship broker logs to CloudWatch and S3.
Set min.insync.replicas=2 with replication.factor=3 for durability.
Use MSK Serverless for variable workloads, provisioned clusters for predictable traffic.
Broker instance selection can be guided by use case:
| Type | Use Case | Partitions/Broker |
|---|---|---|
| kafka.t3.small | Dev/test | Up to 300 |
| kafka.m5.large | Production | Up to 1,000 |
| kafka.m5.2xlarge | High throughput | Up to 2,000 |
Serverless MSK with Terraform for Variable Workloads
AWS automatically manages capacity, scaling, and broker infrastructure for Serverless. You do not need to choose instance types or manage brokers.
The solution provisions a complete Amazon MSK infrastructure, including the VPC, subnets, security groups, IAM roles, the MSK cluster, and a client EC2 instance. With a small set of Terraform commands, you can reliably create, update, and decommission the entire environment in a consistent and repeatable manner across environments.
Configuring a remote Terraform backend using Amazon S3 to securely store and manage the Terraform state is covered as part of multi-environment workflows.
Automating Topic Provisioning and Configuration
In this post, we provide a comprehensive guide for using Terraform to manage Amazon MSK configurations. We explore the process of installing Terraform on Amazon Elastic Compute Cloud, defining and decentralizing topic configurations, and deploying and updating configurations in an automated manner.
Automation allows Terraform to automate the creation, modification, and deletion of MSK topics. Consistency and repeatability ensure consistent topic structures and settings across your entire Amazon MSK environment. This simplifies management and reduces the likelihood of configuration drift.
Scalability enables you to provision and manage large numbers of MSK topics, facilitating growth of your Amazon MSK environment. Version control allows tracking changes, rolling back if needed, and collaborating effectively on your Amazon MSK infrastructure.
Remote State, Backend, and Multi-Environment Workflows
Remote state management is a core part of production workflows. It also covers configuring a remote Terraform backend using Amazon S3 to securely store and manage the Terraform state.
An AWS Account with programmatic access is required. AWS credentials are configured locally using the AWS CLI.
Terraform is straightforward to set up and Terraform is easy to install.
A secure Amazon MSK Serverless cluster can be deployed alongside an EC2 instance configured with Kafka tools and authentication. IAM roles and security groups enable secure communication between the EC2 instance and the MSK cluster.
Conclusion
Building Amazon MSK with Terraform shifts Kafka infrastructure from manual console steps to versioned, repeatable code. Provisioned clusters give fine-grained control over instance type, broker count, storage, encryption at rest with KMS, encryption in transit with TLS, client authentication via SASL IAM and TLS, and broker logging to CloudWatch and Firehose. Serverless removes capacity planning for variable workloads while Terraform still codifies networking, security groups, IAM roles, and state management.
The combination of remote S3 state, modular project structure with main.tf, variables.tf, outputs.tf, and reusable modules, and automated topic provisioning delivers consistency and repeatability across dev, test, and production. Durability is reinforced by min.insync.replicas=2 with replication.factor=3, and observability is supported by Prometheus monitoring and log shipping to CloudWatch and S3. MSK clusters take 15-30 minutes to create, so automation and drift detection become critical operational advantages.
Using Terraform for MSK topic configuration management streamlines operations, minimizes errors, and provides a robust and scalable Amazon MSK environment that can be reliably created, updated, and decommissioned across multiple environments.