Data Platform Reference Architecture

Overview

This reference architecture defines a comprehensive, generic Data Platform designed to scale seamlessly and break down organizational data silos. Structured from the foundational infrastructure up to the end consumers, it outlines the essential capabilities required to ingest, store, process, and serve data, wrapped in robust governance, security, and operational pipelines.

Component Model Layers

1. Data Sources Layer

This foundational layer represents the diverse origins of data that exist before entering the platform. These sources generate the raw information that feeds the enterprise.

COMPONENT	DESCRIPTION	EXAMPLES
Operational Databases	The transactional (OLTP) relational and non-relational databases backing core business applications.	Oracle, SQL Server, PostgreSQL (Open Source), MongoDB
Applications	Cloud-based SaaS products, COTS (Commercial Off-The-Shelf) applications, and internal line-of-business systems.	Salesforce, SAP, Workday, Zendesk
Streams	Continuous, high-velocity data generated by IoT sensors, application telemetry, clickstreams, and social media feeds.	IoT Devices, Web Servers, Mobile Apps
Structured Data	Highly organized data residing in fixed fields, such as spreadsheets, flat files, or legacy mainframe exports.	CSV, Excel, XML
Unstructured Data	Data without a predefined data model, such as video files, audio recordings, images, and text documents.	PDFs, JPEGs, MP4s, Free-text logs

2. Infrastructure Layer

The underlying physical or virtualized hardware and cloud services that host the data platform. This layer provides the foundational compute, storage, and networking capabilities on which all other layers depend.

COMPONENT	DESCRIPTION	EXAMPLES
Cloud Infrastructure	The baseline compute instances, managed services, and object storage that host all data platform workloads. The primary deployment model for modern data platforms.	AWS, Microsoft Azure, Google Cloud (GCP)
Container Orchestration	Schedules and manages containerized data workloads, enabling consistent, portable deployment of processing jobs and services across environments.	Amazon EKS (AWS), Azure AKS (Azure), Google GKE (GCP), Kubernetes (Open Source)
Private Networking	Isolates platform components within a secure virtual network perimeter using private endpoints, VPC peering, and firewall rules to prevent data exfiltration.	AWS VPC, Azure Private Link, Google Cloud VPC, Cilium (Open Source)
On-Premises / Hybrid	Co-located or private data centre hardware for organizations with data residency requirements, latency-sensitive workloads, or legacy systems that cannot be migrated to cloud.	AWS Outposts, Azure Stack, Google Anthos, VMware, Nutanix

3. Data Ingestion Layer

This layer extracts data from diverse source systems, acting as the primary consumer of the Enterprise Integration Platform for event-driven and API-based feeds. Note on Data Contracts: Across all ingestion methods, this layer acts as the enforcement point for Data Contracts—ensuring incoming data adheres to agreed-upon schemas and SLAs before it enters the platform.

COMPONENT	DESCRIPTION	EXAMPLES
Database Ingestion	Utilizes Change Data Capture (CDC) and ETL patterns to securely replicate database changes in near real-time or scheduled batches without impacting operational systems.	AWS DMS (AWS), Azure Database Migration Service (Azure), Google Database Migration Service (GCP), Fivetran, Airbyte (Open Source)
Stream & Messaging Ingestion	Subscribes to the Integration layer's message brokers and Event Stream Processing (ESP) topics to buffer and ingest high-velocity data in real-time.	Amazon Kinesis (AWS), Azure Event Hubs (Azure), Google Pub/Sub (GCP), Confluent, Apache Kafka (Open Source)
API Ingestion	Uses Integration adapters, API Gateways, and webhooks to poll SaaS endpoints or allow third parties to securely push data into the platform.	Amazon AppFlow (AWS), Azure Logic Apps (Azure), Google Cloud Data Fusion (GCP), MuleSoft, Meltano (Open Source)
File Ingestion	Leverages Secure File Transfer protocols and managed transfer services to ingest flat files and unstructured data into cloud storage.	AWS Transfer Family (AWS), Azure Data Factory (Azure), Google Storage Transfer Service (GCP), rclone (Open Source)

4. Data Storage Layer

This layer provides scalable, decoupled storage for all data types. It supports everything from raw object storage to highly structured analytical models and specialized operational formats.

COMPONENT	DESCRIPTION	EXAMPLES
Data Lake	Highly scalable object storage holding all data. Modern implementations utilize Open Table Formats (e.g., Apache Iceberg, Delta Lake, Hudi) layered on top of the storage to bring ACID transactions and warehouse-like reliability to the lake. Separated into Raw, Transformed, and Curated zones.	Amazon S3 (AWS), Azure Data Lake Storage / ADLS (Azure), Google Cloud Storage (GCP), MinIO (Open Source)
Enterprise Data Warehouse (EDW)	A highly structured, relational storage system optimized for complex analytical querying and historical reporting.	Amazon Redshift (AWS), Azure Synapse Analytics (Azure), Google BigQuery (GCP), Snowflake, Apache Hive (Open Source)
Streaming Broker	Persistent, distributed log storage that retains streaming data (from hours to months, depending on retention policy) before it is processed or pushed to long-term storage.	Amazon MSK (AWS), Azure Event Hubs (Azure), Google Pub/Sub (GCP), Confluent, Apache Kafka (Open Source)
Purpose-Built Databases	Specialized databases optimized for serving specific data formats to consumers, including Document DBs, Graph/Network DBs, and Geospatial DBs.	Amazon Neptune (AWS), Azure Cosmos DB (Azure), Google Firestore (GCP), MongoDB, PostGIS (Open Source)
Vector Store	Specialized databases designed to store, index, and query high-dimensional vector embeddings—crucial for modern semantic search, RAG, and generative AI pipelines.	Amazon OpenSearch / RDS pgvector (AWS), Azure Cosmos DB / AI Search (Azure), Vertex AI Vector Search (GCP), pgvector, Qdrant, Milvus, Chroma (Open Source)

5. Data Processing Layer

This layer encompasses the compute engines responsible for transforming, cleaning, enriching, and modeling the data. It also includes the heavy computational lifting required for artificial intelligence.

COMPONENT	DESCRIPTION	EXAMPLES
Distributed Batch Processing	Scalable compute frameworks used to process massive volumes of data in parallel, executing complex ETL/ELT transformations.	Amazon EMR (AWS), Azure Databricks (Azure), Google Dataproc (GCP), dbt, Apache Spark (Open Source)
Stream Processing	Computes and transforms unbounded streams of data in real-time or near real-time, enabling immediate anomaly detection and alerting.	Amazon Kinesis Data Analytics (AWS), Azure Stream Analytics (Azure), Google Dataflow (GCP), Apache Flink (Open Source)
ML Model Training	Scalable, GPU-backed environments where data scientists use raw and processed data to build, train, and tune machine learning models. Distinct from inference — trained models are then served via the Data Consumption layer.	Amazon SageMaker (AWS), Azure Machine Learning (Azure), Google Vertex AI (GCP), Kubeflow (Open Source)

6. Data Consumption Layer

This layer exposes the processed data and trained models to the end consumers. Note on Data Contracts: Interfaces exposed here (like APIs and outbound feeds) are governed by outbound Data Contracts, guaranteeing reliability for downstream consumers.

COMPONENT	DESCRIPTION	EXAMPLES
Data Queries	Ad-hoc, federated SQL query engines that allow users to query data directly in the Data Lake without moving it to a Data Warehouse.	Amazon Athena (AWS), Azure Synapse Analytics (Azure), Google BigQuery (GCP), Trino (Open Source)
Data Visualization	Business Intelligence (BI) tools for creating interactive dashboards, paginated reports, and visual metrics.	Amazon QuickSight (AWS), Microsoft Power BI (Azure), Google Looker (GCP), Tableau, Apache Superset (Open Source)
Model Serving & Advanced Analytics	Hosted machine learning inference endpoints serving real-time predictions and generative model results to client systems, alongside complex statistical analysis and exploratory data science workloads.	Amazon SageMaker Endpoints (AWS), Azure ML Endpoints (Azure), Google Vertex AI Endpoints (GCP), BentoML (Open Source)
Data Activation / Reverse ETL	Pushes transformed, curated analytical data (e.g., customer churn risk) out of the data warehouse back into operational SaaS applications for business action.	Hightouch, Census, Simon Data, Amazon AppFlow (AWS)
Operational Data Stores (ODS)	Read-optimized serving stores that cache curated, pre-aggregated data products for low-latency retrieval by customer-facing applications — bridging the analytical platform and operational systems.	Amazon DynamoDB (AWS), Azure Cache for Redis (Azure), Google Cloud Bigtable (GCP), Apache Cassandra (Open Source)
Data APIs	Securely governed GraphQL or REST APIs that expose data products to internal or external systems.	Amazon API Gateway (AWS), Azure API Management (Azure), Google Apigee (GCP), Hasura (Open Source)

7. Data Consumers Layer

The topmost layer, representing the end-users and systems that derive business value from the data platform.

Business Users: Analysts, executives, and operational staff relying on BI dashboards and reports for decision-making.
Data Scientists: Advanced users running complex models, exploratory data analysis (EDA), and feature engineering experiments.
Applications: Internal applications utilizing data for personalization, search, recommendations, or operational logic.
External 3rd Parties: Customers, vendors, or partners consuming data via shared dashboards or B2B APIs.
Automated Systems: ML-driven pipelines and bots that consume model outputs or data feeds without human intervention.
Technical Operations: Support and engineering teams monitoring application logs, pipeline health, and platform telemetry.

Cross-Cutting Layers

These layers span horizontally across the platform, ensuring data remains secure, compliant, understandable, and operations run smoothly.

Data Governance Layer

COMPONENT	DESCRIPTION	EXAMPLES
Data Discovery & Metadata	Centralized Data Catalogs that store schemas, definitions, and business glossaries, allowing users to easily find and understand data.	AWS Glue Data Catalog (AWS), Microsoft Purview (Azure), Google Dataplex (GCP), Collibra, Apache Atlas (Open Source)
Data Quality	Automated testing and monitoring of datasets to ensure accuracy, completeness, and reliability before the data reaches consumers.	Amazon Deequ (AWS), Azure Purview DQ (Azure), Google Dataplex DQ (GCP), Monte Carlo, Great Expectations (Open Source)
Data Lineage	Tracks the origin, transformations, and downstream usage of data to support impact analysis and regulatory audits.	Microsoft Purview (Azure), Google Dataplex (GCP), DataHub (Open Source), OpenLineage (Open Source)
Master & Reference Data Management	Establishes a single, authoritative point of reference for critical shared data entities (e.g., "Customer") and standardized look-up values.	Profisee, TIBCO EBX, Talend MDM, Reltio

Data Security Layer

COMPONENT	DESCRIPTION	EXAMPLES
Access Control	Fine-grained, database-style authorization governing who can view specific tables, rows, or columns based on their role. Enforces least-privilege access across both human users and service accounts.	AWS Lake Formation (AWS), Microsoft Purview (Azure), Google BigQuery IAM (GCP), Immuta, Apache Ranger (Open Source)
Data Masking & Encryption	Obfuscates Personally Identifiable Information (PII) dynamically during queries and ensures data is encrypted at rest and in transit using platform-managed or customer-managed keys.	AWS KMS (AWS), Azure Key Vault (Azure), Google Cloud KMS (GCP), Snowflake Dynamic Data Masking, Apache Ranger (Open Source)
Secrets Management	Centrally stores, rotates, and audits credentials, API keys, connection strings, and certificates used by platform services. Prevents hardcoded secrets in code or configuration.	AWS Secrets Manager (AWS), Azure Key Vault (Azure), Google Secret Manager (GCP), HashiCorp Vault (Open Source)
Data Classification & Sensitive Data Discovery	Automatically scans datasets to detect and tag sensitive content such as PII, PHI, and financial data. Provides the classification metadata that drives masking and access control policies.	Amazon Macie (AWS), Microsoft Purview (Azure), Google Cloud DLP (GCP), Varonis
Audit Logging	Captures a tamper-evident record of all data access, query execution, and administrative actions across the platform. Essential for compliance reporting (GDPR, HIPAA, SOX) and forensic investigation.	AWS CloudTrail (AWS), Azure Monitor Logs (Azure), Google Cloud Audit Logs (GCP), OpenSearch (Open Source)
Identity & Access Management (IAM)	The foundational enterprise directory used to authenticate human users and machine identities accessing the platform. Governs who can access what, issuing credentials and enforcing policies across all platform components.	AWS IAM & Identity Center (AWS), Microsoft Entra ID (Azure), Google Cloud IAM (GCP), Okta, Keycloak (Open Source)

Supporting Services (DataOps) Layer

COMPONENT	DESCRIPTION	EXAMPLES
Data Orchestration & Workflow Management	The "conductor" of the platform. Automates and schedules multi-step data processing pipelines, manages task dependencies, and handles retries.	Amazon MWAA (AWS), Azure Data Factory (Azure), Google Cloud Composer (GCP), Apache Airflow (Open Source)
Monitoring & Observability (incl. FinOps)	Monitors pipeline health, data freshness, and infrastructure uptime. Includes cost management capabilities to ensure cloud resources and queries remain highly cost-efficient.	Amazon CloudWatch (AWS), Azure Monitor (Azure), Google Cloud Operations Suite (GCP), Datadog, Grafana (Open Source)
CI/CD & Source Control	Version control and automated deployment pipelines for data transformation code, models, and platform configurations.	AWS CodePipeline (AWS), Azure DevOps (Azure), Google Cloud Build (GCP), GitHub Actions, Jenkins (Open Source)
Infrastructure as Code (IaC)	Tools to programmatically provision, update, and manage the cloud infrastructure and data platform components securely and consistently.	AWS CloudFormation (AWS), Azure Bicep (Azure), Google Cloud Deployment Manager (GCP), Pulumi, OpenTofu (Open Source)
Data Modeling Tools	Visual or code-based IDEs used by architects and engineers to design database schemas, star schemas, and data vaults.	Erwin, Hackolade, SqlDBM, pgModeler (Open Source)

Data Platform Component Model