This architecture illustrates a fully automated AWS cloud platform built with Terraform and managed through a GitOps workflow using ArgoCD.

The design spans multiple AWS accounts — Production, Staging, Sandbox, and Operations — each isolated by dedicated VPCs, IAM boundaries, and encrypted storage for maximum security and compliance.

Core components include Amazon EKS clusters running Bottlerocket nodes, Aurora PostgreSQL, ElastiCache Redis, and CloudFront for global delivery.

Security and governance are enforced through AWS WAF, GuardDuty, and Secrets Manager, while observability is powered by Prometheus, Grafana, Loki, and CloudWatch.

The entire platform is provisioned via modular Terraform code, following AWS best practices for automation, reusability, and least-privilege access — delivering a scalable, resilient, and operations-ready foundation for modern applications.

This architecture represents a production-ready, AWS-native CDC pipeline designed for real-time data replication, transformation, and analytics. The design philosophy centers on serverless-first principles, minimizing operational overhead while maximizing scalability and cost-efficiency through native AWS service integration.

Change Data Capture (CDC) begins at the source: AWS DMS reads Aurora PostgreSQL Write-Ahead Logs (WAL) to capture database changes in real-time without impacting source performance. This approach eliminates the need for polling or batch extraction, ensuring sub-second latency for data replication. The CDC stream is then routed toKinesis Data Streams (for managed real-time streaming) or MSK (for Kafka-compatible architectures), providing flexibility to choose based on throughput requirements and existing Kafka expertise.

The ETL layer employs a multi-tool strategy optimized for different workloads:Glue Streaming ETL with Apache Spark handles complex transformations at scale,Lambda functions process lightweight, event-driven transformations with minimal latency, andAppFlow integrates SaaS platforms (Salesforce, ServiceNow, etc.) without custom code. Transformed data is written to S3 Data Lake in columnar formats (Parquet/ORC) for optimal query performance, while Redshift serves as the high-performance data warehouse for analytical queries requiring complex joins and aggregations.

The analytics layer provides multiple query interfaces: Athena offers serverless SQL queries directly against S3 data without infrastructure management, QuickSight delivers interactive BI dashboards with built-in ML insights, and Redshift Spectrum enables Redshift clusters to query petabytes of S3 data without loading it into the warehouse. This hybrid approach optimizes costs by keeping hot data in Redshift and cold data in S3, querying both seamlessly.

Governance and monitoring are embedded throughout: Glue Catalog maintains a centralized metadata repository, enabling schema evolution and data discovery. Lake Formation enforces fine-grained access controls, column-level security, and audit logging across S3 data. CloudWatch monitors pipeline health, stream throughput, Lambda invocations, and DMS replication lag, providing end-to-end observability. This architecture ensuresdata lineage, compliance, and operational excellence while maintaining the agility needed for modern data-driven applications.

This architecture represents a comprehensive, production-grade MLOps pipeline built entirely on AWS native services. The design follows MLOps best practices for model lifecycle management, from experimentation and training to deployment and continuous monitoring, ensuring reproducibility, scalability, and operational excellence.

The pipeline begins with SageMaker Studio, providing collaborative Jupyter notebooks and integrated development environments for data scientists. SageMaker Experiments automatically tracks every training run, capturing hyperparameters, metrics, and artifacts to enable reproducible experiments. The training layer supports distributed training jobs, automated hyperparameter tuning, and AutoML capabilities, allowing teams to optimize model performance efficiently.

Model management is handled through SageMaker Model Registry, which provides version control, approval workflows, and model lineage tracking. Model artifacts are stored in S3, while container images are managed in ECR, enabling consistent deployment across environments. The deployment layeroffers multiple serving options: real-time endpoints for low-latency inference, batch transform for large-scale predictions, and serverless APIs via Lambda and API Gateway for cost-effective, event-driven inference.

Monitoring and governance are critical for production ML systems: CloudWatchcaptures real-time metrics, logs, and performance indicators. SageMaker Clarify analyzes model predictions for bias, fairness, and explainability, ensuring ethical AI practices. SageMaker Model Monitor continuously detects data drift and concept drift, automatically triggering retraining workflows when models degrade.

The orchestration layer coordinates the entire ML lifecycle: SageMaker Pipelinesdefine reusable ML workflows with built-in data processing, training, and validation steps. Step Functions orchestrates complex multi-step workflows across services, while EventBridge enables event-driven automation, triggering pipelines based on model drift alerts, scheduled retraining, or data availability events. This architecture ensures automated, scalable, and reliable ML operations from development to production.