Case Study

Databricks Cost and Performance Optimization for Smarter Healthcare Analytics

A leading healthcare solutions provider modernizes its analytics platform—improving risk identification, efficiency, and compliance

Databricks Cost and Performance Optimization for Scalable, Data-Driven Healthcare

A prominent healthcare organization streamlined its data platform and workflows—reducing operational costs, optimizing resource utilization, and accelerating the delivery of actionable insights for improved decision-making and operational efficiency.

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Industry & Region: Healthcare Analytics, US

Tech Stack: Integration: Data & Analytics Platform: Databricks (with Unity Catalog), Azure DevOps, Jira; Programming Languages & Frameworks: Python, SQL, .NET; Framework: Spark; Database: Azure Database; Project Management: Azure DevOps and Jira.

Client Overview

Our client is a leading practice management solution provider, working closely with health plans, healthcare providers, and community health workers to drive positive outcomes in healthcare. Their advanced analytics platform enables early identification of at-risk cases, enhancing care delivery through predictive intelligence and data-driven insights.

Business Challenge

The organization faced rising cloud costs and increasing operational complexity due to:

  • Complex and difficult-to-manage data pipelines led to potential errors and delayed processing, hampering reliability.
  • Isolated jobs and non-orchestrated workflows, resulting from lack of a centralized database, fostered operational silos.
  • Scattered data sources and unclear dependency management resulted in poor scalability and elevated operational risks.
  • Absence of automated monitoring and reporting prevented timely tracking of files received, processed, and the generation of accurate KPI reports for both business and technical teams.
  • Lack of uniform data governance and security measures increased exposure to compliance and cybersecurity risks.
  • Manual monitoring and management of multiple disconnected workflows raised operational overheads.
  • Inefficient KPI tracking reduced visibility into pipeline health, business-critical metrics, and limited continuous improvement opportunities.
Solution Offered

Our expert team applied advanced data engineering, AI/ML, and analytics expertise to modernize and optimize the client’s data estate across Databricks and Azure. Here’s how we enhanced data pipeline efficiency and performance, optimized compute costs, streamlined resource management, and centralized data governance and security:

  • Modernized Databricks Workflow – We restructured their Databricks workflow using Ephemeral clusters, which were tailored to their work needs. It resulted in faster start-up times and improved performance.
  • Pipeline Modernization & Automation – This was achieved by implementing Databricks workflow orchestration, which enabled clear dependencies and centralized management. Integrating Azure DevOps and Jira further aligned the environment with modern DevOps practices.
  • Automated Deployment Process – Deployment process was automated through rigorous planning, testing, and robust frameworks to address hurdles in the process, along with automation and refactoring of the existing Databricks setup and code to ensure efficiency and scalability.
  • Centralized Data Governance & Security – Standards were implemented by leveraging the Unity Catalog. It provided a centralized governance layer across workspaces, offering fine-grained access controls at the table, column, and row levels that our client had desired. This not only strengthened security but also enabled scalability and ensured compliance. Additionally, data lineage tracking, auditing, and improved data discoverability were established, ensuring transparency and trust in data usage.
  • Optimized Code Integration – Code was optimized to enable interaction with the company’s other UI (.NET application), thereby restricting access to Databricks code and reducing the need to access or modify jobs in Databricks UI.
  • New Job Control Page – A new Job Control Page was created in the UI application to run, monitor, and give control over jobs from any task in the workflow.

The teams worked in close collaboration through regular meetings and Azure DevOps-driven project management. Challenges around integration and automation were addressed through proactive monitoring and iterative delivery.

Value Delivered
  • Reduced cloud costs and improved operational efficiency through intelligent resource management
  • Modularized code and improved Databricks pipeline performance, ensuring greater operational stability
  • Enhanced data quality, trust, and visibility for strategic decision-making
  • Stronger governance and centralized governance with Unity Catalog for compliance and scalability
  • Faster democratization of data
  • Better visibility into data operations

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Discover the analysis results and our recommendations that helped the healthcare organization maximize its ServiceNow ROI.

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Discover the analysis results and our recommendations that helped the healthcare organization maximize its ServiceNow ROI.

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