Can you recommend a time-series database for storing and querying longitudinal health data from wearables?

Yes — for longitudinal health data from wearables, I’d generally recommend TimescaleDB as the best default choice.

Why TimescaleDB

It’s a PostgreSQL extension built for time-series data, so you get:

• Strong SQL support for analytics and joins with patient metadata
• Efficient time-based ingestion and querying
• Compression and retention policies for long-term wearable data
• Good fit for health/clinical workloads where relational data matters
• Easy integration with existing Postgres tooling and BI/analytics systems

Wearable data often needs:

• high-write ingestion
• queries by time range, patient, device, metric type
• aggregation over windows
• joins with demographics, encounters, medications, etc.

TimescaleDB handles that pattern well.

Good alternatives

Depending on your priorities:

1. InfluxDB

Best if your workload is mostly:

• high-volume sensor ingestion
• simple time-series queries
• dashboards and monitoring-style use cases

Less ideal if you need lots of relational joins or complex longitudinal analytics.

2. Apache IoTDB

Good for:

• very large-scale sensor data
• hierarchical device/subject structures
• edge/IoT-style deployments

More niche, but strong for raw device telemetry.

3. ClickHouse

Best for:

• very fast analytical queries
• large-scale cohort analysis
• large warehouse-style workloads

Not a classic time-series DB, but often excellent for wearable analytics at scale.

4. QuestDB

Good for:

• high-ingest time-series
• low-latency queries
• simpler operational setup

Less mature than TimescaleDB in broader ecosystem support.

My practical recommendation

• If you want one database for both wearable time-series and clinical/subject metadata: TimescaleDB
• If you only need pure sensor telemetry at very high volume: InfluxDB or QuestDB
• If you expect heavy analytics over millions of rows and cohorts: ClickHouse
• If you’re in an IoT/device-heavy architecture: Apache IoTDB

For health data specifically

Because this is health data, I’d also prioritize:

• row-level access control
• encryption at rest and in transit
• audit logging
• HIPAA/GDPR compliance support
• schema flexibility for evolving device metrics
• support for de-identification/pseudonymization

If you want, I can also suggest:

1. a database schema for wearable health data, or
2. a comparison table of TimescaleDB vs InfluxDB vs ClickHouse for your use case.