Introduction

Initially each home service brand was associated with a different POS that created challenges with unstructured and dissimilar data. Aggregated real time data into one AWS data lake through multiple POS API integrations.

• Data extraction

• Data aggregation

• Realtime storage

• BI feeders

• Quick insights

• Initially each home service brand was associated with a different POS that created challenges with unstructured and dissimilar data

• Aggregated real time data into one AWS data lake through multiple POS API integrations

• Implemented horizontal and vertical scaling to accommodate peak periods and future business growth

• Democratized data to enable each and everyone to have access to insights

 Technological Framework

Why these technologies?

A combination of AWS and GCP data tools with Timescale for time series data and DBT for transformation enables real-time aggregation and BI-ready insights across 23+ brands, 40+ territories, and 5000+ franchises simultaneously.

Why this Setup?

Django and Python provide the application logic needed to process and serve aggregated KPI data to multiple brand dashboards enabling each franchise to access their own insights without exposing other brands' data.

Why this Setup?

Multiple POS API integrations aggregate disparate, unstructured data from 8+ different systems into a single AWS data lake democratising data access across 23+ brands and 5000+ franchises.

Takeaway

Zapcom aggregated real-time data from 8+ POS systems into a unified AWS data lake for a 23+ brand, 5000+ franchise home services provider enabling democratised data access and KPI insights across 40+ territories.