Enterprise Databricks Cost Optimization for a Global QSR

Quick overview (TL;DR) 

• Client: A global quick‑service restaurant (QSR) 

• Technologies used: Databricks, and Amazon Web Services (AWS) 

• Goal: Introduce cost control and visibility across an expanding Databricks environment while maintaining platform stability and scalability. 

• Challenges: Limited insight into cost drivers, significant volumes of unused data, and uncertainty around which optimization actions could be implemented safely. 

• Solution: Wavicle helped the QSR establish sustainable Databricks cost optimization through cost analysis, targeted data cleanup, automation, and close collaboration with internal teams. 

• Results: With Wavicle’s solution, the QSR stabilized Databricks and cloud spending, reduced unnecessary data footprint, and established a more predictable and manageable operating model, without impacting platform stability or scalability. 

Challenges 

The QSR enterprise expanded its use of Databricks across product teams and regions to support growing analytics needs. Over time, platform and cloud costs rose year over year, but the underlying causes were not clearly understood. 

Although basic cost guardrails existed, the organization lacked detailed insight into usage patterns, storage growth, and inefficiencies at scale. Several key questions remained unanswered: 

• Which workloads and datasets were driving most of the Databricks and cloud costs?
• How much unused or outdated data was being retained in storage?
• Which cost‑reduction actions could be taken safely without impacting daily business operations? 

These uncertainties created financial risk, reduced confidence in budget forecasting, and made it difficult to plan long‑term platform optimization initiatives. 

Solution 

Wavicle partnered with the QSR as a Databricks platform administration and cost‑optimization specialist, focusing on long‑term sustainability rather than one‑time cost reductions. This partnership spanned Databricks environments running on AWS, with a focus on consistent governance and cost optimization across cloud platforms. 

In addition to cost optimization initiatives, Wavicle supported the QSR through broader Databricks platform administration, applying governance, access controls, and operational best practices to ensure the platform remained secure, stable, and scalable. 

Step‑by‑step execution 

1. Cost & usage analysis 

Wavicle analyzed Databricks usage and AWS cloud costs to understand where expenses were coming from. This analysis revealed large amounts of unused data, outdated datasets, and inefficient storage practices. 

The initiative also improved visibility into usage and spend patterns, enabling teams to better understand how resources were consumed and identify opportunities for ongoing optimization. 

2. Data cleanup & storage optimization 

The team safely reduced storage usage by: 

• Removing obsolete files and unused tables
• Reducing unnecessary historical versions (Delta Lake time travel)
• Cleaning up old and non‑current data files
• Aligning data retention with actual business usage 

All cleanup activities were carefully executed to avoid impacting active workloads. 

3. Smart automation for cost control 

To ensure savings continued over time, Wavicle enabled AWS S3 Intelligent Tiering. 

• Unused data automatically moved to lower‑cost storage
• Manual effort was reduced
• Future cost spikes were prevented as data volumes increased

4. Collaborative working model 

Wavicle worked closely with internal teams to: 

• Understand how data was being used
• Protect business‑critical workflows
• Educate teams on better data and storage practices
• Build long-term cost awareness 

In parallel, Wavicle supported ongoing platform operations by assisting with user onboarding, day‑to‑day platform support, and coordination with Databricks to help resolve platform‑level issues and maintain overall platform health. 

Result 

Wavicle’s engagement confirmed that enterprise‑scale Databricks cost optimization was both technically sound and operationally sustainable. The initiative clearly demonstrated where immediate cost reductions were possible and where long‑term controls were required to prevent future cost growth. 

By combining cost visibility, targeted data cleanup, automated storage optimization, and strong platform governance, the organization moved from reactive cost management to a controlled and predictable operating model. The engagement eliminated uncertainty around cost drivers and established a foundation for confident platform scaling. 

Beyond measurable cost savings, the engagement helped improve confidence in platform operations and reinforced cost‑aware data practices across teams. 

The table below summarizes the measurable outcomes achieved through Wavicle’s optimization approach. 

Cost optimization impact summary 

Overall, the engagement strengthened the health of the Databricks platform, stabilized spending patterns, and enabled the organization to operate and scale its data environment with greater confidence and control. 

Next steps   

Building on the outcomes of the cost optimization initiative and ongoing platform administration efforts, the organization outlined the following areas of continued focus: 

• Maintaining visibility into Databricks usage and cloud spend
• Continuing collaboration with product teams
• Incrementally strengthening governance and operational standards
• Evaluating additional opportunities for optimization and platform improvements