HealthTech runs on data. From patient vitals and lab results to insurance claims and wearable device streams, there’s a constant firehose of information flowing in. And with that comes a big responsibility: handling it all quickly, securely, and at scale.
But here’s the catch: this isn’t just any kind of data. It’s highly sensitive, regulated, and often messy. A small hiccup in your pipeline could delay diagnosis, violate HIPAA, or break your app’s core features.
That’s why building a solid, scalable data pipeline is non-negotiable. Whether you’re building a fitness tracking app or a clinical decision support system, your pipeline is the invisible backbone.
In this post, we will walk through exactly how to architect one that’s fast, reliable, and built for the real-world chaos of HealthTech.
Define your use case and data sources
Before jumping into architecture diagrams and tool choices, take a step back. What exactly are you building? A real-time vitals monitoring app? A platform for remote diagnostics? A claims processing system?
Your use case defines everything else from how much data you collect to how fast you need to process it. This is especially true if you’re offering HealthTech software development services, where each project might involve different types of healthcare data, workflows, and compliance needs.
Ask:
- What kind of data are you dealing with? EHRs, HL7/FHIR data, lab reports, wearables, medical images, billing records?
- Where is it coming from? Hospital systems, APIs, IoT sensors, third-party integrations?
- How often does it arrive? Real-time (like heart rate from a wearable)? Daily batches (like insurance claim files)?
- What format is it in? Structured (CSV, SQL), semi-structured (JSON, HL7), or unstructured (images, PDFs)?
Nailing down these details will help you choose the right ingestion, storage, and processing tools. It also sets the stage for compliance and performance planning later on.
Core components of a scalable data pipeline
Now that you know what data you’re handling and where it’s coming from, it’s time to build the backbone: your pipeline.
Building a scalable data pipeline for HealthTech demands a robust infrastructure capable of handling diverse data, from EHRs to real-time inputs from online doctor consultations. This pipeline must efficiently ingest, process, and store vast amounts of sensitive patient data, prioritizing security and regulatory compliance at every stage.
Think of this like assembling a relay team. Each component has a specific role in getting data from point A to point B.
1. Ingestion layer
This is your data entry point. It pulls in raw data from multiple sources.
- Real-time: Apache Kafka, AWS Kinesis, MQTT (great for IoT/wearables).
- Batch: Apache NiFi, SFTP drops, Redox (for healthcare APIs).
- Healthcare-specific: Mirth Connect or HL7 listeners for legacy hospital systems.
2. Storage layer
Where does the data go once it’s in?
- Transactional (for quick lookups): PostgreSQL, MongoDB.
- Analytical (for reporting and ML): Snowflake, BigQuery, Amazon Redshift.
- Raw, flexible storage: AWS S3, GCP Cloud Storage (cheap and scalable for large files like DICOM).
3. Processing layer
This is where data gets cleaned, transformed, and made usable.
- Batch processing: Apache Spark, dbt, Pandas (great for nightly jobs).
- Streaming: Apache Flink, Kafka Streams (for alerts, dashboards).
- Workflow orchestration: Apache Airflow, Prefect, Dagster.
4. Analytics & BI layer
Make your data visible.
- Self-serve dashboards: Looker, Tableau, Power BI.
- Custom UIs: Streamlit, React + Chart.js.
- Embedded insights: Into clinician portals, patient apps, or internal tools.
5. Machine learning layer (Optional)
Want to predict readmission risk? Flag anomalies? Tools like Vertex AI, Databricks, or AWS SageMaker integrate well with your pipeline. Don’t forget: ML pipelines need versioning, retraining workflows, and governance.
Each layer should be loosely coupled but tightly integrated. You want flexibility without fragility. Also, make it modular. You don’t want to re-architect every time a new data source is added or an API version changes.
Key architecture patterns
The right architecture pattern can make or break your scalability, performance, and reliability.
Here are three proven patterns worth considering:
1. Lambda architecture
Best when you need both real-time and historical views of your data.
- Batch layer: Processes large chunks of data periodically for accuracy.
- Speed layer: Handles real-time data for immediate insights (e.g., patient vitals alerting).
- Serving layer: Merges the two for end-user access.
Remote patient monitoring with both trend analysis and real-time alerts is a great use case for this.
2. Event-driven architecture
Here, services communicate by sending events (not requests), which decouples your systems.
The Publish-Subscribe (Pub-Sub) model is where services emit events (e.g., “new lab result received”) that others can subscribe to. This is handy for async processing and scaling microservices. An example of this is a system that triggers a follow-up test request whenever a lab result crosses a critical threshold.
3. Microservices + message queues
Each piece of your pipeline is a self-contained service, and queues buffer communication.
Tools like RabbitMQ, Kafka, and Google Pub/Sub can help isolate failures and make scaling easier. Case in point: A pipeline that parses HL7 messages, enriches them with metadata, and routes them to a secure data lake.
If you’re starting small, don’t overengineer. Keep it simple and modular. Pick a pattern that suits your latency needs, data volume, and team expertise.
Tools and tech stack (with pros & cons)
Let’s be honest, there’s no shortage of tools out there. But in HealthTech, you need to pick the ones that actually get the job done without creating a mess later. That means thinking about scale, compliance, team skills, and future maintenance.
Start with ingestion. If you’re streaming a lot of real-time data (like vitals from wearables), tools like Apache Kafka or AWS Kinesis work great. Kafka is super scalable, but can be overkill if you’re just starting out. Kinesis is simpler, especially if you’re already on AWS. For healthcare-specific data, like HL7 or FHIR, Mirth Connect or Redox are safer bets. Redox makes integration feel like plug-and-play, but it’s a paid tool.
Next, your storage layer. Use PostgreSQL for structured, relational data like patient records. If you’re dealing with semi-structured stuff (like device JSON), MongoDB offers flexibility. Want cheap and scalable raw storage? Amazon S3 is perfect for backups, logs, and large files like DICOMs. For analytics, go with Snowflake or BigQuery. Both are fast and handle scale well, but watch your usage; they’re not cheap if left unchecked.
For processing and transformations, use dbt if you’re working mostly in SQL. It’s clean, version-controlled, and easy to onboard new team members. For more complex workflows or massive data volumes, Apache Spark is powerful, but it needs setup. Orchestrators like Airflow, Prefect, and Dagster help you schedule and manage jobs. Airflow is the most mature. Prefect is more Pythonic and flexible. Dagster has great testing features, but its ecosystem is still growing.
When it comes to BI and visualization, you’ve got options. Tableau is polished and powerful. Power BI is great if you’re in the Microsoft ecosystem. Looker is ideal for teams that care about data modeling and governance. Want something quick and open-source? Try Metabase, not flashy, but it works.
Finally, don’t forget compliance and data governance. Tools like Immuta and Collibra help manage access and track data lineage. They’re more common in bigger orgs, but worth knowing. If you’re dealing with multiple regulations (HIPAA, GDPR, etc.), platforms like OneTrust make audits way less painful.
The bottom line? Pick what fits your stack and your team. Use just enough tech to stay fast, secure, and sane.
Wrapping up
HealthTech is messy. Tons of data, tons of rules, and zero room for error. That’s why your pipeline matters. You need your data to move from point A to point B quickly, cleanly, and without breaking anything.
The trick? Don’t overcomplicate it. Start with a clear use case. Pick tools your team can actually handle. Build for flexibility, not just performance. And above all, bake in compliance and observability from the start because fixing that stuff later is a nightmare.
In the end, a solid pipeline keeps your app running and also helps clinicians make better decisions, keeps patients safer, and gives your team peace of mind.
