A medical service pricing transparency company struggled with rising Databricks costs as their platform scaled. Blueprint’s Lakehouse Optimizer (LHO) was implemented to improve cost visibility and resource management. Within two weeks, the company saw a 35% reduction in overspend, positioning them for efficient growth and long-term success.
Client Snapshot
Who:
Healthcare pricing transparency company
Industry:
Healthcare
Stakeholder(s):
VP of Data Engineering
Work Summary
What we did:
- Implemented the Lakehouse Optimizer (LHO)
- Improved governance, right-sized clusters, and enabled scalable long-term growth
The challenge
Our client, a medical service pricing transparency company, was facing significant challenges with managing the growing complexity and costs associated with scaling their Databricks environment. As their platform processed increasing volumes of healthcare pricing data to provide real-time insights, the associated Databricks cost began to increase significantly. Their attempts at cost monitoring were slow and labor intensive resulting in cycles of ineffective investigation into overages. The client‘s Databricks users began to experience inefficient cluster usage, over-provisioned resources, and an inability to predict or control costs. The client wanted to position themselves for scaled success as their customer base was growing, but needed to first optimize their Databricks environment to support enhanced data processing capabilities.
The solution
We implemented the Blueprint Lakehouse Optimizer (LHO), one of our technology accelerators designed specifically to optimize Databricks environments by providing end-to-end cost monitoring, performance optimization recommendations, and architecture and governance guidance. LHO leveraged advanced cost monitoring frameworks that provided granular visibility into cluster utilization, job execution patterns, and resource allocation for BOTH the client’s Databricks and cloud resources.
Key features of the solution included:
Real-time cost insights:
Detailed alerts and recommendations display in real-time cost and telemetry data at the job and cluster level, allowing for proactive cost management.
Intelligent forecasting:
LHO’s intelligent forecasting anticipates future usage trends and guides resource planning as the platform continues to scale.
Dynamic resource optimization:
Automated recommendations for right-sizing clusters based on usage patterns, minimizing over-provisioning.
Automated alerts & governance:
Custom cost thresholds and automated alerts for overspend, enabling swift action before costs escalate.
Results
2 weeks
to see immediate value
$140k
annual cost savings
35%
Reduction in Databricks overspend
The client achieved a 35% reduction in Databricks overspend within the first two weeks of deploying LHO, which was critical for their goal of scaling efficiently. Beyond the immediate cost savings, the customer’s Databricks platform is now set up for long-term success with enhanced governance and cost visibility. With optimized resource allocation and predictive scaling insights, the client can confidently expand their Databricks workloads as they continue to ingest more data and enhance their platform’s capabilities. This positions them for sustainable growth while maintaining cost control, ensuring their company remains competitive and scalable.
