Modernizing Healthcare Data with a Lakehouse Architecture
A large healthcare provider was working with data dispersed across multiple systems — MySQL databases, REST APIs, and unstructured RTF documents used in clinical and administrative workflows. Most of their processing was manual, slow, and difficult to maintain. Reporting required extra effort, and teams struggled to get reliable insights when they needed them.
The Challenge
The hospital’s data environment had grown quickly, but without a unified structure to manage it. This created several issues:
- Manual data extraction that caused delays
- Inconsistent data quality across multiple sources
- No centralized storage or scalable foundation for analytics
- RTF documents that couldn’t be used for reporting
- Limited security and governance practices
- Slow reporting cycles that impacted operational decisions
They needed a modern, stable, and secure data platform that could automate pipelines, improve quality, and support day-to-day and leadership-level reporting.
Our Approach
We planned a Lakehouse-based modernization strategy designed specifically for healthcare workflows.
Our goal was to bring all structured and unstructured data into one organized space, automate ingestion, improve governance, and make analytics simpler for both operational and leadership teams.
The approach was built around clear data flows, strong validation, and tools that felt easy for the hospital staff to adopt.
The Solution
Automated and Scalable Data Pipelines : We built ETL pipelines to ingest data from MySQL and REST APIs, using Python over SSH tunnels for secure extraction. Validation and exception handling ensured consistency across all incoming data.
Airflow-Based Orchestration : Apache Airflow on EC2 was deployed to automate scheduling, retries, and monitoring. Email alerts and logs helped maintain reliability without manual effort.
Databricks Lakehouse Architecture : We designed a structured Bronze → Silver → Gold Delta Lake foundation with encryption, data masking, schema validation, and lineage tracking — supporting healthcare-grade governance.
LLM-Powered Processing for RTF Documents : Large RTF documents, such as clinical or operational reports, were converted into structured JSON using LLM classification. This unlocked new use cases for analytics that previously weren’t possible.
Dimensional Modeling : We built fact and dimension models tailored to hospital operations, ensuring fast reporting and smooth aggregations.
Power BI Dashboards : Interactive dashboards were created on the Gold layer, offering real-time, refresh-enabled insights for both routine operations and higher-level planning.
CI/CD with Databricks Asset Bundles : Deployment of notebooks, workflows, and pipelines was automated across development, QA, and production.
Security, Monitoring & Optimization : We set up logging, error tracking, governance controls, and performance tuning, ensuring a reliable and cost-efficient environment.
The Impact
The healthcare provider now runs on a modern, unified Lakehouse platform that supports their growing data needs.
Key improvements include:
- 85% reduction in manual effort through automation
- Daily availability of clean, trusted Gold-layer data
- Faster and more accurate reporting
- New analytics enabled through structured RTF data
- Scalable architecture ready for future expansion
- Leadership teams gained better visibility through dynamic dashboards
The entire data lifecycle — from extraction to reporting — became smoother, more secure, and far easier for hospital teams to work with.
Modernizing Healthcare Data with a Lakehouse Architecture
A large healthcare provider was working with data dispersed across multiple systems — MySQL databases, REST APIs, and unstructured RTF documents used in clinical and administrative workflows. Most of their processing was manual, slow, and difficult to maintain. Reporting required extra effort, and teams struggled to get reliable insights when they needed them.
The Challenge
The hospital’s data environment had grown quickly, but without a unified structure to manage it. This created several issues:
- Manual data extraction that caused delays
- Inconsistent data quality across multiple sources
- No centralized storage or scalable foundation for analytics
- RTF documents that couldn’t be used for reporting
- Limited security and governance practices
- Slow reporting cycles that impacted operational decisions
They needed a modern, stable, and secure data platform that could automate pipelines, improve quality, and support day-to-day and leadership-level reporting.
Our Approach
We planned a Lakehouse-based modernization strategy designed specifically for healthcare workflows.
Our goal was to bring all structured and unstructured data into one organized space, automate ingestion, improve governance, and make analytics simpler for both operational and leadership teams.
The approach was built around clear data flows, strong validation, and tools that felt easy for the hospital staff to adopt.
The Solution
Automated and Scalable Data Pipelines : We built ETL pipelines to ingest data from MySQL and REST APIs, using Python over SSH tunnels for secure extraction. Validation and exception handling ensured consistency across all incoming data.
Airflow-Based Orchestration : Apache Airflow on EC2 was deployed to automate scheduling, retries, and monitoring. Email alerts and logs helped maintain reliability without manual effort.
Databricks Lakehouse Architecture : We designed a structured Bronze → Silver → Gold Delta Lake foundation with encryption, data masking, schema validation, and lineage tracking — supporting healthcare-grade governance.
LLM-Powered Processing for RTF Documents : Large RTF documents, such as clinical or operational reports, were converted into structured JSON using LLM classification. This unlocked new use cases for analytics that previously weren’t possible.
Dimensional Modeling : We built fact and dimension models tailored to hospital operations, ensuring fast reporting and smooth aggregations.
Power BI Dashboards : Interactive dashboards were created on the Gold layer, offering real-time, refresh-enabled insights for both routine operations and higher-level planning.
CI/CD with Databricks Asset Bundles : Deployment of notebooks, workflows, and pipelines was automated across development, QA, and production.
Security, Monitoring & Optimization : We set up logging, error tracking, governance controls, and performance tuning, ensuring a reliable and cost-efficient environment.
The Impact
The healthcare provider now runs on a modern, unified Lakehouse platform that supports their growing data needs.
Key improvements include:
- 85% reduction in manual effort through automation
- Daily availability of clean, trusted Gold-layer data
- Faster and more accurate reporting
- New analytics enabled through structured RTF data
- Scalable architecture ready for future expansion
- Leadership teams gained better visibility through dynamic dashboards
The entire data lifecycle — from extraction to reporting — became smoother, more secure, and far easier for hospital teams to work with.
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