Deploying Debezium on AWS ECS or Fargate

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Debezium’s change data capture capabilities transform databases into event streams, enabling real-time data pipelines, microservices synchronization, and event-driven architectures. While Kafka Connect provides the standard deployment model for Debezium connectors, running this infrastructure on AWS demands careful consideration of container orchestration options. ECS (Elastic Container Service) and Fargate offer distinct approaches to deploying Debezium—ECS provides granular control over EC2 instances while Fargate abstracts infrastructure entirely. Understanding the architectural considerations, networking requirements, and operational tradeoffs between these platforms enables building robust, production-ready CDC pipelines that leverage AWS-native services effectively.

The challenge of deploying Debezium on AWS extends beyond simply containerizing Kafka Connect. Database connectivity requirements, persistent storage for connector offsets, high availability considerations, and integration with AWS services like RDS, MSK (Managed Streaming for Kafka), and Secrets Manager create deployment complexity. ECS and Fargate each handle these concerns differently—ECS offers flexibility through EC2 instance control but requires managing cluster capacity, while Fargate simplifies operations at the cost of networking constraints and higher per-task costs. This guide explores both approaches in depth, providing the architecture patterns, configuration examples, and operational guidance needed to deploy production-grade Debezium CDC pipelines on AWS container platforms.

Architecture Fundamentals

Debezium Deployment Components

A complete Debezium deployment consists of several interconnected components that must coordinate effectively. Kafka Connect acts as the runtime environment for Debezium connectors, managing connector lifecycle, configuration, and coordination among distributed workers. Multiple Connect workers form a cluster that distributes connector tasks across instances, providing both scalability and fault tolerance.

The source databases—MySQL, PostgreSQL, MongoDB, or other supported systems—represent the data origins that Debezium monitors for changes. Debezium connectors establish database connections using native protocols to capture transaction logs without impacting application performance. These connections require network accessibility, authentication credentials, and sufficient permissions to read replication streams or binary logs.

Kafka brokers receive change events from Debezium and persist them durably. AWS MSK (Managed Streaming for Apache Kafka) provides fully managed Kafka infrastructure that eliminates operational overhead while integrating with VPC networking, CloudWatch monitoring, and IAM authentication. Alternatively, self-managed Kafka on EC2 offers more control at the cost of increased operational responsibility.

Storage for connector state, offsets, and configuration requires reliable persistence. Kafka Connect traditionally uses Kafka topics for this metadata, but considerations around topic replication, retention, and access control affect deployment design. The offset storage topic captures the position in database logs, making it critical for exactly-once semantics and recovery after failures.

ECS vs Fargate Deployment Models

ECS on EC2 provides complete control over underlying infrastructure, allowing optimization of instance types, networking configuration, and resource allocation. You manage the EC2 cluster, selecting instance sizes that match Debezium’s memory and CPU requirements. This model suits workloads requiring specialized instances, persistent EBS storage, or fine-grained cost optimization through reserved instances or spot capacity.

Fargate abstracts infrastructure entirely, launching containers without managing servers. You define task CPU and memory requirements, and AWS provisions resources automatically. This serverless approach simplifies operations but constrains networking—Fargate tasks run in awsvpc network mode only, requiring ENI (Elastic Network Interface) allocation per task. Fargate also costs more per vCPU-hour than equivalent EC2 capacity, though it eliminates instance management overhead.

The decision between ECS and Fargate hinges on several factors: operational complexity tolerance, cost optimization requirements, networking constraints, and team expertise. Organizations prioritizing operational simplicity often choose Fargate despite higher costs, while teams with container platform expertise and cost sensitivity prefer ECS with EC2. Both approaches can deploy production-grade Debezium, but implementation details differ significantly.

Architecture Components Overview

🗄️
Source Database
RDS MySQL/PostgreSQL
or self-managed
📦
Kafka Connect
Debezium connectors
on ECS/Fargate
🔄
Kafka Cluster
AWS MSK or
self-managed
👁️
Monitoring
CloudWatch Logs
and Metrics

ECS Deployment Implementation

Container Image Preparation

Building a production-ready Debezium container image requires including Kafka Connect, Debezium connectors, and necessary dependencies. Start with the official Debezium Connect image as a base, adding connector plugins and configuration management capabilities:

FROM debezium/connect:2.5
LABEL maintainer="your-team@company.com"

# Set environment variables for AWS
ENV AWS_REGION=us-east-1
ENV JAVA_OPTS="-Xms512m -Xmx2g"

# Install AWS CLI for secrets management
USER root
RUN microdnf install -y aws-cli && \
    microdnf clean all

# Add custom connector configurations
COPY connector-config/ /kafka/config/

# Add healthcheck script
COPY healthcheck.sh /usr/local/bin/
RUN chmod +x /usr/local/bin/healthcheck.sh

# Switch back to kafka user
USER kafka

# Configure Kafka Connect
ENV CONNECT_BOOTSTRAP_SERVERS="MSK_BOOTSTRAP_SERVERS"
ENV CONNECT_GROUP_ID="debezium-cluster"
ENV CONNECT_CONFIG_STORAGE_TOPIC="debezium-cluster-configs"
ENV CONNECT_OFFSET_STORAGE_TOPIC="debezium-cluster-offsets"
ENV CONNECT_STATUS_STORAGE_TOPIC="debezium-cluster-status"
ENV CONNECT_KEY_CONVERTER="org.apache.kafka.connect.json.JsonConverter"
ENV CONNECT_VALUE_CONVERTER="org.apache.kafka.connect.json.JsonConverter"
ENV CONNECT_REST_ADVERTISED_HOST_NAME="localhost"
ENV CONNECT_REST_PORT="8083"
ENV CONNECT_PLUGIN_PATH="/kafka/connect"

# Replication factors for production
ENV CONNECT_CONFIG_STORAGE_REPLICATION_FACTOR="3"
ENV CONNECT_OFFSET_STORAGE_REPLICATION_FACTOR="3"
ENV CONNECT_STATUS_STORAGE_REPLICATION_FACTOR="3"

HEALTHCHECK --interval=30s --timeout=10s --retries=3 \
  CMD /usr/local/bin/healthcheck.sh

EXPOSE 8083

The healthcheck script verifies Kafka Connect readiness by checking the REST API:

#!/bin/bash
# healthcheck.sh
curl -f http://localhost:8083/ || exit 1

This container image includes AWS CLI for retrieving secrets, configures Connect for distributed mode, and implements health checking for ECS task health validation.

ECS Task Definition Configuration

The ECS task definition specifies container configuration, resource allocation, networking, and IAM permissions. Create a task definition that provides appropriate resources for Debezium workloads:

{
  "family": "debezium-connect",
  "networkMode": "awsvpc",
  "requiresCompatibilities": ["EC2"],
  "cpu": "2048",
  "memory": "4096",
  "executionRoleArn": "arn:aws:iam::ACCOUNT_ID:role/ecsTaskExecutionRole",
  "taskRoleArn": "arn:aws:iam::ACCOUNT_ID:role/debeziumTaskRole",
  "containerDefinitions": [
    {
      "name": "debezium-connect",
      "image": "ACCOUNT_ID.dkr.ecr.REGION.amazonaws.com/debezium-connect:latest",
      "cpu": 2048,
      "memory": 4096,
      "essential": true,
      "portMappings": [
        {
          "containerPort": 8083,
          "protocol": "tcp"
        }
      ],
      "environment": [
        {
          "name": "CONNECT_BOOTSTRAP_SERVERS",
          "value": "b-1.msk-cluster.kafka.REGION.amazonaws.com:9092,b-2.msk-cluster.kafka.REGION.amazonaws.com:9092"
        },
        {
          "name": "CONNECT_GROUP_ID",
          "value": "debezium-cluster"
        },
        {
          "name": "AWS_REGION",
          "value": "us-east-1"
        }
      ],
      "secrets": [
        {
          "name": "DATABASE_PASSWORD",
          "valueFrom": "arn:aws:secretsmanager:REGION:ACCOUNT_ID:secret:rds/mysql-password"
        }
      ],
      "logConfiguration": {
        "logDriver": "awslogs",
        "options": {
          "awslogs-group": "/ecs/debezium-connect",
          "awslogs-region": "us-east-1",
          "awslogs-stream-prefix": "debezium"
        }
      },
      "healthCheck": {
        "command": [
          "CMD-SHELL",
          "curl -f http://localhost:8083/ || exit 1"
        ],
        "interval": 30,
        "timeout": 5,
        "retries": 3,
        "startPeriod": 60
      }
    }
  ]
}

This task definition uses awsvpc networking for VPC integration, references secrets from AWS Secrets Manager for database credentials, and configures CloudWatch Logs for centralized logging.

IAM Roles and Permissions

The execution role enables ECS to pull images and retrieve secrets, while the task role grants permissions for Kafka Connect operations:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "kafka-cluster:Connect",
        "kafka-cluster:AlterCluster",
        "kafka-cluster:DescribeCluster"
      ],
      "Resource": "arn:aws:kafka:REGION:ACCOUNT_ID:cluster/msk-cluster/*"
    },
    {
      "Effect": "Allow",
      "Action": [
        "kafka-cluster:*Topic*",
        "kafka-cluster:WriteData",
        "kafka-cluster:ReadData"
      ],
      "Resource": "arn:aws:kafka:REGION:ACCOUNT_ID:topic/msk-cluster/*"
    },
    {
      "Effect": "Allow",
      "Action": [
        "kafka-cluster:AlterGroup",
        "kafka-cluster:DescribeGroup"
      ],
      "Resource": "arn:aws:kafka:REGION:ACCOUNT_ID:group/msk-cluster/*"
    },
    {
      "Effect": "Allow",
      "Action": [
        "secretsmanager:GetSecretValue"
      ],
      "Resource": "arn:aws:secretsmanager:REGION:ACCOUNT_ID:secret:rds/*"
    },
    {
      "Effect": "Allow",
      "Action": [
        "logs:CreateLogGroup",
        "logs:CreateLogStream",
        "logs:PutLogEvents"
      ],
      "Resource": "arn:aws:logs:REGION:ACCOUNT_ID:log-group:/ecs/debezium-connect:*"
    }
  ]
}

These permissions enable Kafka Connect to interact with MSK using IAM authentication and retrieve database credentials securely.

Service Configuration and Scaling

Create an ECS service that manages task lifecycle, health checks, and scaling:

aws ecs create-service \
  --cluster debezium-cluster \
  --service-name debezium-connect \
  --task-definition debezium-connect:1 \
  --desired-count 3 \
  --launch-type EC2 \
  --network-configuration "awsvpcConfiguration={
    subnets=[subnet-abc123,subnet-def456],
    securityGroups=[sg-debezium],
    assignPublicIp=DISABLED
  }" \
  --health-check-grace-period-seconds 120 \
  --deployment-configuration "maximumPercent=200,minimumHealthyPercent=100" \
  --enable-ecs-managed-tags \
  --propagate-tags SERVICE

This service configuration runs three Connect workers for high availability, uses rolling deployments to minimize downtime, and integrates with VPC networking for database and MSK access.

Fargate Deployment Implementation

Fargate-Specific Considerations

Fargate deployment differs from ECS on EC2 primarily in networking and resource allocation. Every Fargate task receives its own ENI, consuming IP addresses from your VPC subnets. Plan subnet CIDR ranges carefully to accommodate the maximum expected task count, considering that ENIs persist briefly after task termination.

Fargate tasks support only awsvpc network mode, simplifying security group rules but requiring sufficient subnet capacity. Each task gets an independent network interface, enabling fine-grained security policies but consuming more IP addresses than bridge or host networking.

Task CPU and memory combinations are constrained to specific valid pairs in Fargate. Debezium workloads typically need 2-4 vCPU and 4-8 GB memory for production use. Consult AWS documentation for valid combinations—attempting invalid pairs causes task launch failures.

Modified Task Definition for Fargate

The task definition for Fargate closely resembles ECS but requires the Fargate compatibility specification:

{
  "family": "debezium-connect-fargate",
  "networkMode": "awsvpc",
  "requiresCompatibilities": ["FARGATE"],
  "cpu": "2048",
  "memory": "4096",
  "executionRoleArn": "arn:aws:iam::ACCOUNT_ID:role/ecsTaskExecutionRole",
  "taskRoleArn": "arn:aws:iam::ACCOUNT_ID:role/debeziumTaskRole",
  "containerDefinitions": [
    {
      "name": "debezium-connect",
      "image": "ACCOUNT_ID.dkr.ecr.REGION.amazonaws.com/debezium-connect:latest",
      "essential": true,
      "portMappings": [
        {
          "containerPort": 8083,
          "protocol": "tcp"
        }
      ],
      "environment": [
        {
          "name": "CONNECT_BOOTSTRAP_SERVERS",
          "value": "b-1.msk-cluster.kafka.REGION.amazonaws.com:9092,b-2.msk-cluster.kafka.REGION.amazonaws.com:9092"
        },
        {
          "name": "CONNECT_REST_ADVERTISED_HOST_NAME",
          "value": "0.0.0.0"
        }
      ],
      "secrets": [
        {
          "name": "DATABASE_PASSWORD",
          "valueFrom": "arn:aws:secretsmanager:REGION:ACCOUNT_ID:secret:rds/mysql-password"
        }
      ],
      "logConfiguration": {
        "logDriver": "awslogs",
        "options": {
          "awslogs-group": "/ecs/debezium-connect-fargate",
          "awslogs-region": "us-east-1",
          "awslogs-stream-prefix": "debezium"
        }
      },
      "healthCheck": {
        "command": [
          "CMD-SHELL",
          "curl -f http://localhost:8083/ || exit 1"
        ],
        "interval": 30,
        "timeout": 5,
        "retries": 3,
        "startPeriod": 60
      }
    }
  ]
}

Note that cpu and memory are specified at the task level for Fargate rather than per-container, and the requiresCompatibilities includes FARGATE.

Fargate Service Creation

Creating the service for Fargate requires platform version specification and appropriate networking:

aws ecs create-service \
  --cluster debezium-fargate-cluster \
  --service-name debezium-connect-fargate \
  --task-definition debezium-connect-fargate:1 \
  --desired-count 3 \
  --launch-type FARGATE \
  --platform-version LATEST \
  --network-configuration "awsvpcConfiguration={
    subnets=[subnet-abc123,subnet-def456],
    securityGroups=[sg-debezium],
    assignPublicIp=DISABLED
  }" \
  --health-check-grace-period-seconds 120 \
  --deployment-configuration "maximumPercent=200,minimumHealthyPercent=100"

Fargate eliminates cluster capacity management but requires careful subnet planning to ensure adequate IP address availability for scaling.

ECS vs Fargate Comparison

ECS on EC2
Pros:
• Lower cost at scale
• Instance-level control
• Flexible networking
• EBS persistent storage

Cons:
• Manage EC2 capacity
• Longer deployment times
• Complex scaling logic
Fargate
Pros:
• No infrastructure management
• Fast task startup
• Simplified operations
• Isolated networking

Cons:
• Higher per-task cost
• Limited CPU/memory combos
• ENI limits in subnets

Networking and Security Configuration

VPC and Subnet Design

Debezium tasks require network connectivity to three systems: source databases, Kafka brokers, and the Kafka Connect REST API for management. Design VPC architecture with private subnets for ECS tasks, avoiding public IP assignment for security. Use NAT gateways if tasks need outbound internet access for pulling images or accessing external services.

Security groups control network traffic between components. Create dedicated security groups for Debezium tasks, database instances, and MSK clusters. The Debezium security group needs outbound access to database ports (3306 for MySQL, 5432 for PostgreSQL) and Kafka broker ports (9092 for plaintext, 9094 for TLS). Database security groups should allow inbound traffic from Debezium security groups on appropriate ports.

MSK cluster configuration affects networking significantly. Private MSK clusters accessible only from within the VPC work well with ECS and Fargate tasks deployed in the same VPC. Public MSK endpoints enable connectivity from outside the VPC but introduce security considerations. Most production deployments use private MSK with VPC peering or Transit Gateway for multi-VPC access.

Database Connection Configuration

RDS database connectivity requires proper security group rules and authentication configuration. For MySQL, ensure binary log format is set to ROW and binlog retention is adequate. PostgreSQL requires logical replication configuration with appropriate wal_level and max_replication_slots settings.

Database credentials should never be hardcoded in container images or task definitions. Use AWS Secrets Manager to store credentials securely, referencing secrets in task definitions. ECS and Fargate inject secret values as environment variables at task launch, and IAM permissions control which tasks can access which secrets.

Connection pooling and retry logic in Debezium connectors handle transient network issues. Configure connector database.connectionTimeoutInMs and database.connectionAttempts appropriately for your network conditions. RDS maintenance windows or failovers should not cause permanent connector failures—proper retry configuration enables automatic recovery.

Kafka Connect REST API Access

Managing Debezium connectors requires REST API access to Kafka Connect. Deploy an Application Load Balancer in front of ECS/Fargate tasks to provide a stable endpoint for connector management. Configure ALB health checks to verify Connect worker health, routing requests only to healthy tasks.

Authentication for the REST API prevents unauthorized connector modifications. While Kafka Connect doesn’t include built-in authentication, implement API Gateway in front of ALB for IAM-based authentication, or use AWS WAF rules to restrict access by IP address or other criteria.

Internal APIs that don’t require internet exposure should remain private. Place ALBs in private subnets accessible only through VPN, Direct Connect, or VPC endpoints. This prevents exposing the Connect API to the public internet while enabling management from corporate networks or jump hosts.

Operational Considerations

Monitoring and Logging

CloudWatch Logs integration provides centralized log aggregation for Debezium tasks. Configure log groups with appropriate retention policies—30 days for active development, longer for production. Use CloudWatch Insights queries to analyze connector behavior, identify errors, and track change event throughput.

Kafka Connect exposes metrics via JMX that require exporting to CloudWatch. Deploy a sidecar container running a JMX exporter that publishes metrics to CloudWatch Metrics. Track key metrics: connector task status, source record poll rate, source record write rate, and rebalance frequency. Alert on connector task failures or sustained zero throughput.

MSK integration with CloudWatch provides broker-side metrics complementing Connect metrics. Monitor MSK consumer lag to detect if Debezium is falling behind in processing database changes. Persistent lag indicates throughput problems requiring connector performance tuning or increasing Connect cluster capacity.

High Availability and Disaster Recovery

Running multiple Kafka Connect workers provides high availability through task distribution. When a worker fails, Kafka Connect automatically rebalances tasks to healthy workers. Deploy at least three workers across multiple availability zones for production workloads, ensuring continued operation during AZ outages or task failures.

Connector configuration and offset storage in Kafka topics provides durability. The offset storage topic captures the exact position in database logs, enabling recovery to the precise point of failure. Configure appropriate replication factors (minimum 3) for offset, config, and status topics to prevent data loss.

Database failover scenarios require connector reconfiguration when database endpoints change. RDS failovers update DNS automatically, but connection pools may need recycling. Configure connectors with short database connection timeouts and automatic reconnection to minimize downtime during failovers.

Scaling Strategies

Horizontal scaling adds more Connect workers to distribute connector tasks. Each connector can split into multiple tasks that run in parallel on different workers. Configure max.tasks in connector configuration based on data distribution—more tasks provide better throughput for databases with multiple partitions or shards.

Vertical scaling increases resources per task through larger CPU and memory allocations. CPU-bound connectors benefit from additional vCPUs, while memory-intensive workloads need more RAM. Monitor task resource utilization through CloudWatch Container Insights to identify when vertical scaling would help.

Auto-scaling policies for ECS services respond to metrics like CPU utilization or custom metrics like consumer lag. However, Connect cluster scaling requires careful handling—scaling down during active rebalancing can cause disruption. Implement scale-in protection and longer cooldown periods for scale-down operations.

Troubleshooting Common Issues

Connectivity Problems

Connection timeout errors indicate network misconfiguration or security group restrictions. Verify security groups allow traffic from Debezium tasks to databases and Kafka brokers. Use VPC Flow Logs to diagnose rejected connections and identify missing security group rules.

DNS resolution failures occur when VPC DNS settings prevent resolving RDS or MSK endpoints. Ensure ECS tasks use VPC DNS by enabling DNS resolution in VPC settings. For custom DNS, configure Docker container networking to use appropriate DNS servers.

Performance Issues

Slow throughput manifests as high consumer lag or delayed change propagation. Profile connector performance by examining JMX metrics. Insufficient max.tasks limits parallelism—increase tasks to match database sharding. Network bandwidth constraints between tasks and databases or Kafka brokers reduce throughput—use enhanced networking instances or upgrade Fargate task sizes.

Memory exhaustion causes out-of-memory errors visible in CloudWatch Logs. Debezium connectors buffer changes in memory before writing to Kafka. Increase task memory allocation or reduce batch sizes to prevent exhaustion. Monitor JVM heap usage through JMX metrics to right-size memory allocation.

Connector Failures

Task failures appear in ECS service events and CloudWatch Logs. Common causes include invalid connector configuration, insufficient database permissions, or Kafka broker connectivity issues. Review connector logs for error messages indicating the failure cause. Invalid configuration syntax or missing required properties prevents connector start.

Rebalancing storms where connectors repeatedly start and fail indicate persistent errors or resource contention. Check if database replication lag causes timeouts during snapshot phase. Verify Kafka broker capacity handles connector throughput. Increase health check grace periods to allow longer initialization.

Conclusion

Deploying Debezium on AWS ECS or Fargate requires careful consideration of networking, security, resource allocation, and operational requirements that differ significantly from traditional VM-based deployments. ECS on EC2 provides maximum flexibility and cost efficiency for teams comfortable managing container infrastructure, while Fargate offers simplified operations at higher cost, ideal for organizations prioritizing operational simplicity over per-unit costs. Both platforms can support production-grade CDC pipelines when properly configured with appropriate IAM roles, security groups, monitoring, and high availability patterns.

Success with Debezium on AWS container platforms demands going beyond basic containerization to address platform-specific concerns like networking in awsvpc mode, secret management through Secrets Manager, and scaling patterns that respect Connect cluster coordination requirements. The architecture patterns, configurations, and operational guidance provided here enable building robust, maintainable CDC infrastructure that leverages AWS-native services effectively. Whether choosing ECS for cost optimization or Fargate for operational simplicity, understanding the tradeoffs and implementation details of each approach ensures your Debezium deployment meets reliability, performance, and cost targets.

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