Time series data management in smart home systems: balancing real-time analytics and data storage

The article presents a novel approach to managing time-series data in smart home systems that balances real-time analytics with efficient historical data storage. A hybrid architecture combining Azure CosmosDB for real-time data processing and Azure Synapse for historical data analytics demonstrates significant performance advantages. The system achieves up to 165-fold acceleration in analytical query execution (from 3.3 seconds to 20 milliseconds for time-series aggregation queries) while reducing the number of read operations by 17. Implementing optimization strategies, such as state-based recording and interval-based storage, significantly reduces data volume while maintaining temporal data integrity. The system demonstrates linear scalability, handling up to 1 million write operations per second, and achieves compression ratios of up to 5:1 in CosmosDB and 10:1 in Synapse.