IoT, Financial & Analytics
Explore use cases in IoT, finance, and business analytics
Module: Tiger Data 101 → TimescaleDB Core Use Cases
Section: Core Use Cases
Estimated time: 8–10 minutes total (this page: ~3 minutes)
Learning objectives
Section titled “Learning objectives”By the end of this module, you will be able to:
- Identify key use case categories where TimescaleDB excels
- Analyze business problems and determine if TimescaleDB is appropriate
- Evaluate specific use cases and explain how TimescaleDB features address challenges
- Apply patterns from one use case to similar scenarios
IoT & Sensors
Section titled “IoT & Sensors”The challenge: Thousands of devices generating readings every few seconds—millions of rows per day, per deployment.
How TimescaleDB helps:
- hypertables absorb high-velocity writes efficiently
- hypercore compression dramatically reduces historical storage
- Continuous Aggregates power real-time dashboards without scanning raw rows
- Standard SQL makes it easy to JOIN sensor readings with device metadata
Financial Data
Section titled “Financial Data”The challenge: Markets generate enormous volumes with strict latency requirements. Every data point must be preserved for backtesting, audits, compliance.
How TimescaleDB helps:
- hypertables handle high-frequency tick ingestion
- Continuous Aggregates generate OHLC candles at multiple resolutions automatically
- hypercore keeps years of tick history queryable at a fraction of the cost
- Full SQL enables complex analytics without a separate analytics layer
Analytics & Reporting
Section titled “Analytics & Reporting”The challenge: Business intelligence on time-series events (clickstreams, transactions, user activity) needs to be fast and always fresh, without expensive nightly batch jobs.
How TimescaleDB helps:
- Continuous Aggregates replace nightly ETL with auto-refreshing rollups
- Hierarchical CAGGs mean each granularity only reads from the next finer level
- Time-windowed analytics are natural in SQL