---
title: "How Indra unified EV charging data on Databricks"
description: "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."
---

# How Indra unified EV charging data on Databricks

[Databricks](https://yomu.fyi/company/databricks) · Jack Yallop · Aug 28, 2026

**Type:** Problem & solution

## 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.

## Context

Indra’s data estate expanded across multiple Azure services and independently developed pipelines. This increased costs, duplicated logic, fragmentation, maintenance overhead, and governance limitations, while business users remained dependent on the data team for routine metrics and reporting.

## Approach / What changed

Indra consolidated its data workloads on Databricks, using Unity Catalog for governance, Delta tables and a medallion architecture for data processing, PySpark transformations, serverless SQL warehouses, AI/BI dashboards, and Genie Agents. The fleet pipeline moved from multiple Azure Functions to a shared pipeline serving three clients.

## Takeaways

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

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [Azure](https://yomu.fyi/topic/azure), [Monitoring](https://yomu.fyi/topic/monitoring), [Performance](https://yomu.fyi/topic/performance)

- Source: [Databricks](https://www.databricks.com/blog/how-indra-unified-ev-charging-data-databricks)
- Source URL: https://www.databricks.com/blog/how-indra-unified-ev-charging-data-databricks
- Ingested by Yomu: 2026-08-30T12:34:52.364Z

[Read original post](https://www.databricks.com/blog/how-indra-unified-ev-charging-data-databricks)