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Architecture overview - Guidance for an Automotive Data Platform on AWS
Solution ComponentsHigh-Level ArchitectureData Flow ArchitectureAWS Services UsedWell-Architected Framework AlignmentNext Steps

Architecture overview

This chapter provides a high-level overview of the Automotive Data Platform architecture, including the three integrated solutions and their deployment options.

Automotive Data Platform Architecture Overview

Solution Components

The Automotive Data Platform consists of four integrated solutions that can be deployed independently or together:

Automotive Data Mesh

A domain-oriented decentralized data architecture with centralized governance using Amazon SageMaker Unified Studio and Amazon DataZone. This foundation enables cross-domain collaboration, data product discovery, and federated governance across all automotive data domains.

Key capabilities: Data product catalog, self-service data access, automated lineage tracking, federated governance, and collaborative workspaces.

Customer 360 Analytics

Unify customer data from CRM, service, telemetry, and external sources to create comprehensive customer profiles. Enable AI-powered insights through natural language queries using Amazon Bedrock agents and interactive dashboards with Amazon QuickSight.

Key capabilities: Entity resolution, unified customer profiles, AI-powered analytics, and real-time dashboards.

Predictive Maintenance

Predict vehicle component failures using machine learning models trained on connected vehicle telemetry. Enable proactive service scheduling to reduce downtime and improve customer satisfaction using Amazon SageMaker and AWS IoT Core.

Key capabilities: Real-time telemetry ingestion, ML-based failure prediction, proactive service alerts, and dealer integration.

Automotive Data Governance

Implement comprehensive data governance with automated PII detection, multi-region compliance, and fine-grained access control using AWS Lake Formation and Amazon Macie. Support GDPR, CCPA, and regional data residency requirements.

Key capabilities: Automated PII classification, policy-based access control, cross-region governance, and compliance monitoring.

High-Level Architecture

The Automotive Data Platform consists of six layers that work together to provide comprehensive data management, analytics, and AI capabilities:

1. Data Sources Layer

Internal Systems:

  • CRM platforms (Salesforce, Microsoft Dynamics)

  • ERP systems (SAP, Oracle)

  • Dealer management systems

  • Service management platforms

  • Finance and billing systems

  • Collaboration tools

External Systems:

  • Connected vehicle telemetry (IoT Core, MQTT)

  • Warranty claims databases

  • Social media sentiment feeds

  • Third-party market data

  • Supply chain systems

  • Sales and inventory data

2. Data Ingestion Layer

Real-Time Streaming:

Near Real-Time APIs:

Batch Processing:

3. Data Storage and Governance Layer

Data Lake Storage:

  • Amazon S3 for scalable object storage

  • Parquet files for columnar analytics

  • Apache Iceberg tables for ACID transactions

  • Vector embeddings for semantic search

Metadata and Governance:

Security and Compliance:

4. Data Processing Layer

Batch Processing:

Stream Processing:

Entity Resolution:

ML Training and Inference:

  • Amazon SageMaker for model training

  • SageMaker Pipelines for ML workflows

  • SageMaker endpoints for real-time inference

  • SageMaker batch transform for bulk predictions

5. Analytics and AI Layer

SQL Analytics:

Interactive Dashboards:

AI and Machine Learning:

Caching and State Management:

6. Consumption Layer

Business Users:

Data Scientists and Analysts:

Applications and APIs:

Third-Party Systems:

  • Dealer management systems

  • Usage-based insurance platforms

  • Fleet management applications

  • Mobile apps for vehicle owners

Data Flow Architecture

Customer 360 Data Flow

1. Data Sources (CRM, Service, Telemetry)
   ↓
2. Ingestion (API Gateway, Kinesis, Glue ETL)
   ↓
3. Raw Storage (S3 Bronze Layer)
   ↓
4. Entity Resolution (AWS Entity Resolution, Glue ETL)
   ↓
5. Unified Profiles (S3 Silver Layer)
   ↓
6. Aggregated Metrics (S3 Gold Layer)
   ↓
7. Glue Data Catalog + Lake Formation Permissions
   ↓
8. Analytics (Athena Views, Quick Suite Datasets)
   ↓
9. AI Agent (Bedrock Knowledge Base + Agent)
   ↓
10. Business Users (Dashboards, Natural Language Queries)

Predictive Maintenance Data Flow

1. Connected Vehicles (MQTT over IoT Core)
   ↓
2. IoT Core Basic Ingest → Amazon MSK
   ↓
3. Apache Flink (Decode, Decompress, Enrich)
   ↓
4. Processed Telemetry → S3 + ElastiCache
   ↓
5. ML Training Pipeline (Step Functions + SageMaker)
   ↓
6. Trained Model → SageMaker Endpoint
   ↓
7. Inference API (API Gateway + Lambda)
   ↓
8. Predictions → DynamoDB + EventBridge
   ↓
9. Alerts (SNS, Email, Dealer Systems)
   ↓
10. Service Scheduling (Proactive Outreach)

Multi-Region Governance Data Flow

Central Governance Region:
  - Lake Formation (Global Policies)
  - DataZone (Catalog + Lineage)
  - CloudTrail (Audit Logs)
  - Macie (PII Monitoring)
     ↓
EU Producer Region:
  - IoT Core → Kinesis → S3
  - Glue ETL Streaming (PII Classification)
  - PII Data Store (GPS, Driver Info)
  - Anonymized Data Store (Hashed IDs, City-Level)
  - Cognito + API Gateway (Vehicle Owner Access)
     ↓
Global Consumer Region:
  - SageMaker (ML Training on Anonymized Data)
  - Quick Suite (Analytics Dashboards)
  - Lake Formation (Enforce Anonymized-Only Access)

AWS Services Used

This solution uses the following AWS services:

Data Lake and Storage

  • Amazon S3 - Scalable object storage for data lake with support for Parquet, Iceberg, and vector embeddings

  • AWS Glue Data Catalog - Centralized metadata repository for all data assets

  • AWS Lake Formation - Fine-grained access control at database, table, and column level

  • Amazon Athena - Serverless SQL queries with Lake Formation permission inheritance

Data Ingestion

Data Processing

Analytics and Visualization

AI and Machine Learning

Governance and Security

  • Amazon DataZone - Data discovery, lineage tracking, and self-service collaboration

  • AWS CloudTrail - Immutable audit log of all API calls and data access

  • Amazon Macie - Automated PII discovery and classification

  • AWS Security Hub - Centralized security findings and compliance monitoring

  • AWS IAM - Identity and access management for all AWS resources

  • AWS KMS - Encryption key management for data at rest

  • AWS Secrets Manager - Secure storage for database credentials and API keys

Caching and State Management

Authentication and Access

Monitoring and Observability

Well-Architected Framework Alignment

This solution aligns with the AWS Well-Architected Framework across all six pillars:

Operational Excellence

  • CloudFormation/CDK for infrastructure as code

  • CloudWatch for centralized logging and monitoring

  • X-Ray for distributed tracing across ML pipelines

  • Step Functions for visual workflow monitoring

  • Athena query history for audit trails

Security

  • IAM and Lake Formation for fine-grained access control

  • KMS encryption for data at rest

  • VPC isolation for Aurora and SageMaker

  • Secrets Manager for credential rotation

  • Bedrock Guardrails for PII protection

  • CloudTrail for complete audit trails

Reliability

  • S3 with 99.999999999% durability

  • Aurora Multi-AZ for automatic failover

  • SageMaker multi-instance endpoints

  • Step Functions automatic retry logic

  • Serverless architecture eliminates infrastructure failures

Performance Efficiency

  • Athena partition pruning for fast queries

  • Glue Spark for distributed processing

  • Aurora pgvector HNSW indexing for vector search

  • SageMaker GPU instances for ML training

  • CloudFront caching for Quick Suite dashboards

Cost Optimization

  • Serverless architecture (Lambda, Athena, Glue) for pay-per-use

  • S3 Intelligent-Tiering for automatic storage optimization

  • Aurora Serverless v2 for automatic capacity scaling

  • SageMaker auto-scaling for inference endpoints

  • Quick Suite pay-per-session pricing for occasional users

Sustainability

  • Serverless architecture minimizes idle resources

  • Graviton processors for 60% better energy efficiency

  • S3 Intelligent-Tiering reduces storage energy consumption

  • Aurora Serverless v2 scales down during low-traffic periods

  • Deployment in renewable energy AWS Regions

Next Steps

For detailed architecture of each solution, see the respective chapter:

  • Automotive Data Mesh Architecture

  • Customer 360 Analytics Architecture

  • Predictive Maintenance Architecture

  • Automotive Data Governance Architecture

For deployment planning, see the Plan Your Deployment chapter for cost estimates, security considerations, and compliance requirements.