Overcoming Cost Inefficiencies in a Growing Data Platform
The client is a mid-sized European fashion retailer with decades of experience and a strong family-owned culture. Its Business Intelligence team acts as the central hub for data operations, collecting and processing data from online stores, physical outlets, and social media before delivering insights across the organization. The team has historically built and maintained the data platform in-house, which is now being migrated to Databricks as part of this engagement.
As the organization expanded its data capabilities, the existing platform became increasingly difficult to manage, creating both operational inefficiencies and rising costs. What initially supported growth began to introduce significant risks, limiting scalability and slowing down innovation.
- Undocumented knowledge: The platform was built and maintained without documentation, leaving critical knowledge with a small group of individuals and creating operational risk and scalability challenges.
- Rising infrastructure costs without visibility: Inefficient Databricks configuration and underutilized clusters led to growing costs, with no clear insight into usage patterns or cost drivers.
- Legacy architecture limiting scalability: An outdated orchestration and processing framework restricted adoption of modern capabilities and made the platform difficult to evolve.
- Lack of transparency into business value: Limited visibility into data flows, pipeline usage, and impact made it difficult to optimize performance, control costs, or prioritize improvements.
A comprehensive transformation was required to reduce costs, improve governance, and establish a transparent, scalable data platform capable of supporting future growth.
Engineering a Cost-Optimized and Governed Databricks Platform
Addressing these challenges required a holistic transformation of the data platform, combining architectural improvements with governance and cost management capabilities. Our team engaged as a strategic engineering partner to redesign the Databricks environment for efficiency, scalability, and transparency.
1. Optimizing Data Processing and Compute Usage
Workloads were analyzed and restructured to improve efficiency across the platform. Data processing pipelines were optimized to reduce unnecessary computation and improve performance, ensuring that resources were used only when needed.
Improved workload management reduced idle compute usage and minimized redundant processing, lowering overall infrastructure costs while maintaining high performance.
2. Implementing Cost Attribution and Monitoring
A cost attribution framework was introduced to provide visibility into resource consumption across teams and workloads. Each process and dataset could be tracked, enabling better understanding of where costs were generated.
Increased transparency allowed teams to take ownership of their usage and make informed decisions about resource allocation, significantly improving cost control across the organization.
3. Enhancing Data Governance and Lineage
Governance mechanisms were implemented to improve data quality, consistency, and trust. Data lineage capabilities enabled users to track how data was created, transformed, and consumed across the platform.
Improved governance ensured that analytics outputs were reliable and compliant with organizational standards, while also simplifying collaboration between teams.
4. Modernizing Orchestration and Workflow Management
Legacy orchestration processes were replaced with more efficient and scalable workflows. Dependencies between data pipelines were streamlined, reducing complexity and improving reliability.
A modern orchestration approach enabled faster development cycles, easier maintenance, and more predictable execution of data workflows.
5. Building a Scalable Foundation for Advanced Analytics
The optimized platform was designed to support future growth, including advanced analytics, machine learning, and AI-driven use cases. A flexible architecture allows new workloads to be added without significantly increasing operational complexity or cost.
A future-ready data foundation ensures the organization can continue to innovate while maintaining control over performance and spending.
Transforming Data Platform Efficiency into Business Value
The partnership transformed a costly and complex data environment into a streamlined, governed, and scalable platform. Improved visibility into resource usage and cost drivers enables more efficient operations, while optimized workflows deliver faster and more reliable analytics.
Better governance and lineage increase trust in data, allowing teams to collaborate more effectively and make informed decisions with confidence. A cost-efficient architecture ensures that growth in data and analytics capabilities does not translate into uncontrolled spending.
Before
- Split between Databricks and unsupported custom solution; no migration plan
- Under-utilised compute clusters, set up with “max resources, always on” strategy in mind
- €230,000 forecasted for 2026
- No lineage, no cost attribution, no disaster recovery, no production access controls.
After
- Migration actively underway; full Databricks consolidation in progress
- SLA-bound cluster pools aligned with business demands and properly scaled to ETL workloads
- ~€70,000 (approximately 70% reduction)
- Governance framework scoped and sequenced
A scalable and well-governed platform positions the organization to expand its use of AI and advanced analytics, turning data infrastructure into a strategic enabler of business performance.
As part of KMS Technology, Addepto continues to deliver enterprise-grade data and AI solutions that help organizations balance innovation with operational efficiency.
Ready to optimize your data platform? Contact us today!