FinTech · Case study

World-Leading Financial Information Company

Existing tools couldn't keep pace with energy-sector clients. No low-latency engine, no enterprise RBAC, no room to scale.

The context

A world leader in financial information and analytics needed a near real-time analytics application for its energy-sector clients. Existing tools lacked the speed, depth and customisation the use case demanded, and any solution had to handle complex energy data at scale with enterprise-grade role-based access, deployed and maintained end-to-end.

At a glance

Sector
FinTech
Headline result
5technologies benchmarked to select the low-latency engine

What we did

The approach.

  1. 01

    Benchmarked the engine

    Ran a structured proof-of-concept across five candidate technologies, Databricks, Snowflake, SingleStore, Kinetica and Druid, to find the one that could hold near real-time latency at scale.

  2. 02

    Built the multi-engine stack

    Databricks for data transformation, Snowflake for scalable storage, and SingleStore as the low-latency query layer powering live dashboards.

  3. 03

    Embedded the BI layer

    Delivered Energy Studio, a fully embedded BI application built on Apache Superset and Power BI, with enterprise-grade RBAC for secure, role-appropriate access.

  4. 04

    Owned delivery end-to-end

    Handled full deployment and ongoing maintenance directly, so energy-sector clients get intuitive, near real-time dashboards without operating the stack themselves.

The outcome

What changed.

Near real-time

dashboards replacing slow, generic tooling

5-way POC

completed to select the right low-latency engine

Enterprise RBAC

secure, role-based access built in from day one

Let's talk

Want an outcome like this?

A senior engineer on the call from day one. Two weeks to a written architecture and a working reference build.