---
title: "Marketing Data Architecture & Warehouse Ingestion"
url: https://janarraj.com.my/projects/marketing-data-architecture/
date: 2026-09-14
modified: 2026-10-02
lang: en
author: "Janarraj Rajandran"
description: "A structured approach for moving departmental data into a queryable reporting database."
word_count: 232
---

# Marketing Data Architecture & Warehouse Ingestion

[Home](/)/[Automation & Systems](/projects/)/Marketing Data Architecture
{ DATA INFRASTRUCTURE }

# Marketing Data Architecture & Warehouse Ingestion

A structured approach for aggregating disparate advertising, retail POS, e-commerce, and local search data into a queryable relational database warehouse.

ON THIS PAGE
Overview
The Problem
The Solution
Measured Outcomes

## Executive Summary

This infrastructure provides the single source of truth for all multi-branch operational data, eliminating spreadsheet fragmentation and powering real-time cross-functional analytics.

1SINGLE SOURCE OF TRUTH
5+DATA SOURCES UNIFIED
0MANUAL RE-ENTRY ERRORS

THE PROBLEM

### Disconnected Silos & Inconsistent Master Data

- **Isolated Data Islands:** Advertising data lived in Google/Meta, sales lived in POS, local reviews lived in GBP, and branches lived in random spreadsheets.
- **Conflicting Identifiers:** Branches were named differently across systems, preventing automated table joins and unified financial reporting.

THE ARCHITECTURAL SOLUTION

### Normalized Relational Schemas & Automated ETL Pipelines

- **Universal Branch Master:** Established verified UUIDs and normalized branch metadata across Google Drive, BMS, and PostgreSQL.
- **Automated Ingestion Workers:** n8n scheduled workers extract, validate, and load daily records into normalized PostgreSQL tables.
- **Direct BI Connectivity:** Clean SQL views feed Looker Studio and Metabase dashboards directly without manual spreadsheet middle layers.

THE MEASURED OUTCOME

### Enterprise-Grade Analytics & Instant Cross-Channel Visibility

Enabled leadership to evaluate true Customer Acquisition Costs, branch profitability, and inventory velocity across nationwide retail operations.

{ UNIFY YOUR DATA STACK }[Let’s connect↗](/contact/)Tired of stitching together broken spreadsheets?
Let's design a clean relational data architecture.

[Start a conversation ↗](/contact/)