TopN AnalyticsMapping EV Charger Reliability and Demand with GIS Analytics
An EV SaaS provider needed insight into the distribution and reliability of EV chargers across major cities, but their data was scattered across a self-overwriting Google Sheet and a slow production Postgres database.
Where things stood
A live Google Sheet updated every 5 minutes was configured to overwrite existing data instead of appending it, destroying historical records.
The production Postgres database was optimized for transactions, not analytics, and responded extremely slowly to analytical queries.
What we built
We scraped the Google Sheet into BigQuery tables to preserve a full history, and synced the Postgres data into an AWS Redshift OLAP warehouse for fast analytical access.
Using Python, we aggregated charging data by map coordinates and time period to identify charger locations, usage patterns, and reliability metrics.
We built heat maps in Tableau to visually highlight hotspots for additional charger deployment.
How it works
An OLAP data warehouse in AWS Redshift for consolidated, fast analytics, paired with GIS dashboards in Tableau for strategic charger placement decisions.
What we'd do again
Preserving raw history before it's overwritten (via the BigQuery scrape) turned out to be as important as the analytics itself; without it, the reliability metrics would have had major gaps.
Want the full picture?
We're happy to walk through the details, numbers, and trade-offs directly.