AWS Modernization Architecture

A production architecture for data, AI, and multi-tenant SaaS.

The platform migrates from cloud-based data delivery into an AWS-native intelligent data platform built around lakehouse governance, Bedrock Claude, and secure enterprise applications.

Data Backbone

S3 Raw/Cleaned/Curated

Traceable lakehouse zones for source records, quality processing, and business-ready data.

AI Backbone

Bedrock + SageMaker

Claude reasoning with ML forecasting, retrieval context, agents, and guardrails.

App Backbone

EKS Services

Multi-tenant services for BI, AI orchestration, reporting, and data governance.

Reference Architecture

The end-to-end flow is designed as a closed loop: enterprise sources, ingestion, data lake, analytics, AI intelligence, business applications, and security operations.

01 Ingestion

Multi-source ingestion fabric

Connect enterprise operational systems into a unified pipeline while preserving source traceability and freshness requirements.

AWS DMS CDC for incremental database synchronization.
Amazon Kinesis for real-time streams and Amazon MSK for Kafka ecosystems.
Glue Crawlers for batch files, metadata discovery, and catalog registration.

02 Lakehouse

S3 lakehouse with governed layers

All data lands in Amazon S3 as the traceable system of record, then moves through quality-controlled business layers.

Raw layer preserves source fidelity for audit and replay.
Cleaned layer standardizes formats, quality rules, and entity alignment.
Curated layer publishes business-ready datasets and reusable metric assets.

03 Compute

Analytics and computation layer

The computation layer combines OLAP, batch processing, stream aggregation, feature engineering, and embedded BI.

Redshift Serverless for BI aggregation and analytical queries.
EMR Spark for large-scale batch processing and feature pipelines.
Managed Flink for real-time metric aggregation; QuickSight for embedded visualization.

04 Intelligence

Bedrock Claude and ML orchestration

Generative AI is not an add-on. It becomes the reasoning layer that explains metrics, writes reports, supports NL2SQL, and interprets forecasts.

Claude handles insight generation, attribution reasoning, report writing, and data governance assistance.
Knowledge Bases retrieve metric definitions, schemas, and historical reports from OpenSearch Serverless.
SageMaker handles forecasting and anomaly models; Claude turns model output into business language.

Operating Model

End-to-end platform flow

This flow keeps analytical outputs connected to governed data and keeps AI outputs connected to retrievable business context.

Sources

Enterprise operational data enters the platform

Databases, streams, files, SaaS/ERP, and IoT data are ingested with clear ownership.

Govern

Lakehouse layers standardize and govern data

Quality rules, catalogs, lineage, and metric definitions create reusable enterprise assets.

Analyze

Compute services generate metrics and analytical outputs

OLAP, stream computation, and ML pipelines produce BI, alerts, forecasts, and features.

Act

AI and applications deliver decision material

Applications expose dashboards, conversations, reports, data services, and workflow triggers.

Application Layer

Multi-tenant application services

The platform exposes business capabilities through secure application services instead of raw infrastructure endpoints.

Tenant authentication and authorization

CloudFront accelerates access, application services handle REST/WebSocket traffic, and Cognito manages multi-tenant identity.

EKS microservice runtime

Data query, AI orchestration, reporting, governance, tenant management, and node services run as scalable microservices.

State and cache layer

Aurora PostgreSQL stores metadata and configurations; DynamoDB and Redis support high-concurrency access patterns.

Security and Operations

Controls designed across every layer

Security, audit, monitoring, and sensitive-data controls are built into the platform path from ingestion to AI output.

Encryption and least privilege

KMS encryption, IAM least privilege, Secrets Manager, and VPC isolation protect production workloads.

Audit and observability

CloudTrail audits platform activity while CloudWatch monitors pipelines, services, tenant traffic, and AI workflows.

Application and data protection

WAF protects application traffic and Macie helps identify sensitive data in the lakehouse.

Planning an AWS data and AI migration?

We can translate your current platform into a phased modernization architecture and migration plan.

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