How Indra unified EV charging data on Databricks

Summary

Indra Renewable Technologies consolidated a fragmented EV-charging data estate spanning Cosmos DB, Synapse, Azure Data Lake Storage, Azure Functions, Power BI, and other services onto Databricks. The migration began with a parallel Synapse prototype and expanded to Delta tables, a consolidated fleet pipeline, and greater use of Databricks for operational workloads. A medallion architecture stores raw fleet data in bronze, transforms it with PySpark in silver, and publishes gold tables for reporting and client delivery; one pipeline now serves three clients and improved performance by 60% to 70%. Unity Catalog, serverless SQL warehouses, AI/BI dashboards, and Genie Agents support governed self-service analytics, while reported results include 80% to 90% business cost savings, 90% storage savings, and query latency falling from 24.4 seconds to 3.5 seconds.

Takeaways

1. 1

   The medallion fleet architecture uses bronze for raw telemetry and transactions, silver for PySpark transformations, and gold for reporting and client delivery.

2. 2

   Indra reported 80% to 90% business cost savings from migrating from Synapse to Databricks, alongside 90% storage cost savings and per-active-user cost falling from £1.30 to 45p.

3. 3

   AI/BI dashboards and Genie Agents let business, engineering, and product users query governed telemetry, device, firmware, vehicle, and user data without waiting for ad hoc data-team support.