KQL Databases: Advanced Patterns and Techniques
'{ "SoftDeletePeriod": "90.00:00:00", "Recoverability": "Enabled" }' '{ "DataHotSpan": "7.00:00:00", "IndexHotSpan": "14.00:00:00" }'
7 articles
'{ "SoftDeletePeriod": "90.00:00:00", "Recoverability": "Enabled" }' '{ "DataHotSpan": "7.00:00:00", "IndexHotSpan": "14.00:00:00" }'
These patterns form the foundation for building scalable, performant IoT database solutions.
This architecture handles billions of time-series data points while maintaining query performance for both real-time and historical analysis.
For development or small workloads: For production workloads, use AKS with persistent volumes:
TimescaleDB is the time-series database I recommend to teams that are already comfortable with PostgreSQL and don't want to learn a new query language or…
Anomaly Detector is the Cognitive Service I pull out when someone asks "can you tell us when something goes wrong?" and the answer is "I don't know exactly…
A general-purpose database can store time-stamped data. It just gets sad about it once you're past a few hundred million rows and someone wants a five-year…