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PROBLEM

Lack of visibility and understanding of Databricks consumption across the organisaion, leading to spiralling Databricks costs.​

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SOLUTION

​Accelerated FinOps maturity through enterprise-grade observability enabling Alinta Energy to:

  • visualise Databricks consumption and cost at the most granular level

  • identify cost saving opportunities

  • accurately budget and forecast consumption and spend

  • catch silent "cost-creepers" and anomalies - no more bill shock

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OUTCOMES

  • 32% reduction in Alinta Energy's Databricks spend

  • 4x increase in Databricks query performance

  • 98% cost attribution via automated tagging and monitoring

INDUSTRY

Energy and Utilities

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LOCATION

Australia

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SERVICES

FinOps, Observability​

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TECHNOLOGIES

Databricks (Azure), Unity Catalog, PowerBI

“Furo’s solution has been a game-changer for us. The transparency it provides, has not only transformed how we manage our Databricks environment but also shifted our team’s entire approach to cloud spending. We now have full confidence in what we’re spending, knowing it is been proactively optimised.”

​The Customer

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Alinta Energy is one of Australia’s largest energy retailers, generators, investors, and developers. In the last decade they’ve grown from being the largest residential gas retailer in Western Australia to the preferred electricity and gas provider for more than 1.1 million homes and businesses Australia-wide and are determined to make energy better.

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Alinta Data Hub (ADH) strategically leverages the Databricks lakehouse platform as the technical backbone of its enterprise data strategy, enabling advanced analytics, AI-driven decision-making, and operational efficiency across its energy generation and retail operations.

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​The Challenge

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Alinta Data Hub (ADH) adopted the Databricks Lakehouse Platform in 2021 to support its data strategy, driving analytics, AI, and operational efficiency across energy operations. However, as workloads expanded, Alinta Energy struggled with cost governance and lacked FinOps capabilities, leading to uncontrolled spending, inefficiencies, and challenges in attributing costs to business units or projects.

 

Alinta Energy recognised a need for robust cost observability and governance capabilities which was not natively available from Databricks.

 

Key challenges Alinta Energy faced with Databricks cost governance:

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  • Dual Charges:  they incurred two primary charges - licensing fees for Databricks and Azure infrastructure costs. This duality complicates the understanding of total expenses.

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  • Complex Pricing Model: Databricks’ pricing is based on Databricks Units (DBUs), which vary depending on workload types, instance configurations, and cloud providers. This complexity made it difficult to estimate and control costs effectively.

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  • Granular Cost Tracking Issues: they struggled to differentiate costs by specific initiatives or allocate budget accurately across teams/projects resulting in inaccurate forecasts.

 

Alinta Energy engaged Furō to implement cost observability capability to provide better visibility into spending patterns, enable more accurate budgeting and forecasting, and unlock cost savings opportunities by eliminating inefficiencies like over-provisioned clusters or idle resources.

Solution / Approach​​

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Furō FinOps solution for Databricks elevated Alinta Data Hub’s financial operations from reactive oversight to strategic value optimisation, enabling data-driven cost optimisation initiatives.

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Key solution benefits:

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  • Strategic Alignment - Partnered with Alinta to define cost observability KPIs aligned with financial and operational priorities.

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  • Context-Driven Dashboards - Delivered custom analytics mapping Azure & Databricks spend to business units, projects, and workloads.

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  • Real-Time Cost Governance - Enabled granular tracking of Workspace/Job/Cluster-level spend with automated anomaly alerts.

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  • Forecasting Precision - Achieved 98% cost attribution accuracy, empowering data-driven budget planning.

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  • FinOps Automation - Deployed auto-shutdown workflows for idle clusters.

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  • Agile Implementation - Executed iterative development via biweekly sprint cycles with cross-functional stakeholder reviews.

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Outcomes Delivered

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  • 32% cost reduction in annual Azure and Databricks costs

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  • 4x increase in query performance - via serverless transition and compute optimisations

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  • 98% cost attribution - achieved through automated tagging and monitoring.

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Alinta Energy now has unprecedented control over their Databricks spend. For the first time, Alinta Energy now have near real-time access to Databricks Unit (DBU) consumption data, enabling proactive decision-making and more efficient spend management.

 

They can analyse costs by project, environment, and job name, making changes based on usage patterns and tracking the impact before receiving monthly billing reports. This has unlocked a new level of cost optimisation and cultural transformation across their teams.

​"These are outstanding reports, great work."

-- Alinta Energy  - Executive Director of Technology​

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"Setting the bar high for data visualisation. Great work."

-- Alinta Energy - Business Intellience and Operations Manager

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​​​"You guys have set the bar pretty high for the IT team now."

-- Alinta Energy - Data Governance Manager​

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