Project CloudBridge – Day 2: Enterprise Real-Time Analytics Architecture on AWS

Project CloudBridge – Day 2: Enterprise Real-Time Analytics Architecture on AWS

Day 2 of Project CloudBridge focuses on designing an enterprise-grade real-time analytics architecture using AWS database services. Before building anything hands-on, we must think like architects.

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1️⃣ The Enterprise Problem

Organizations need near real-time dashboards, reporting, and analytics — but running heavy queries directly on the production OLTP database can slow down customer-facing applications.

Rule: Never mix heavy analytics workload with OLTP transactions in production.

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2️⃣ Master Architecture – End-to-End Design

This pattern cleanly separates:

• OLTP Layer – Amazon RDS PostgreSQL
• Replication Layer – AWS DMS (Full Load + CDC)
• Analytics Layer – Amazon Redshift
• BI Layer – QuickSight / Tableau / Power BI

Customer Applications ↓ Amazon RDS PostgreSQL (OLTP) ↓ AWS DMS (Full Load + CDC) ↓ Amazon Redshift (Analytics Warehouse) ↓ BI / Reporting / Dashboards

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3️⃣ Amazon RDS PostgreSQL – OLTP Layer

Purpose:

• Handle live transactions (INSERT / UPDATE / DELETE)
• Maintain fast response time for customer-facing apps
• Use Multi-AZ for high availability (production best practice)
• Protect OLTP from reporting workload by isolating analytics

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4️⃣ CDC Flow – PostgreSQL WAL to DMS

CDC (Change Data Capture) reads PostgreSQL transaction logs (WAL) and captures changes such as:

• INSERT
• UPDATE
• DELETE

This enables near real-time synchronization to the analytics platform without repeatedly running full reloads.

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5️⃣ AWS DMS – Full Load + Continuous Replication

DMS typically runs in two phases:

• Full Load – One-time initial data copy
• CDC – Ongoing continuous replication of changes

Operational Note: If DMS stops, OLTP continues normally. Analytics becomes stale until replication resumes.

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6️⃣ Amazon Redshift – Analytics Warehouse

Redshift is designed for fast analytics at scale:

• Columnar storage for efficient scanning
• Massively Parallel Processing (MPP)
• Fast aggregation queries across large datasets
• Easy BI integration (QuickSight / Tableau / Power BI)

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7️⃣ Why This Architecture Is Enterprise-Grade

Layer	Responsibility
RDS PostgreSQL	Transactions (OLTP)
AWS DMS	Replication (Full Load + CDC)
Amazon Redshift	Analytics & Reporting

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🔐 Production Considerations (What Architects Always Add)

• Enable Multi-AZ and automated backups for RDS
• Ensure PostgreSQL WAL settings support CDC requirements
• Monitor DMS replication lag and task health
• Use least-privilege IAM roles for DMS access
• Secure connectivity with VPC Security Groups and encryption in transit

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8️⃣ Day 3 Preview

Next, we go hands-on:

• Create RDS PostgreSQL (production-minded configuration)
• Enable WAL settings needed for CDC
• Create DMS replication instance
• Configure source & target endpoints
• Start Full Load + CDC and validate data flow

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Tags: #AWS #CloudBridge #RDS #PostgreSQL #DMS #CDC #Redshift #RealTimeAnalytics #DataEngineering

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📘 Project CloudBridge – Series Navigation

• Day 1 – Why Enterprises Separate OLTP & Analytics
• Day 2 – Enterprise Real-Time Analytics Architecture
• Day 3 – RDS Production Deployment (Coming Next)

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