The Brief
Healthcare organisations generate large volumes of data from multiple systems — clinical records, monitoring devices, and administrative platforms. The data is rarely in a usable format and almost never in one place.
PulseGrid was built to solve the ingestion, processing, and delivery problem — taking raw healthcare data from multiple sources and producing structured, analytics-ready outputs automatically.
What We Built
PulseGrid is a cloud-native data engineering platform built on AWS, designed around automated ETL pipelines and DevOps principles.
- Multi-source ingestion — connectors for structured and unstructured healthcare data sources
- ETL pipelines — Python-based transformation logic processing data at scale
- Kubernetes orchestration — containerised pipeline jobs with automated scheduling and retry logic
- Terraform infrastructure — all AWS resources provisioned as code, fully reproducible
- Structured data outputs — clean, validated datasets ready for analytics and reporting
- Pipeline monitoring — automated alerting on failures, data quality checks at every stage
Technical Decisions
Kubernetes for pipeline orchestration. Data pipelines need reliable scheduling, retry logic, and resource isolation. Running jobs as containerised Kubernetes workloads gives all three — with horizontal scaling when data volumes spike.
Python for transformation logic. Python’s data ecosystem (pandas, pydantic, boto3) provides the right tools for healthcare data transformation without heavyweight frameworks.
Data quality gates at every stage. Healthcare data errors have real consequences. Validation checks at ingestion, transformation, and output catch problems before they reach downstream systems.
Results
- Automated end-to-end pipeline from ingestion to analytics-ready output
- Zero manual data processing steps in the production workflow
- Scalable architecture handling variable data volumes without intervention
- Full audit trail on every data transformation for compliance purposes