Asset Health Monitoring
DATA ENGINEERING CHALLENGES
Data volume
Managing the massive volume and high velocity of time-stamped data generated by thousands of sensors is challenging for traditional data platforms.
Data lag
Data warehouses or data lakes can easily create hours of data lag, due to the architectural limitation, making them unable to deliver timely, actional insights.
Data Storage Costs
Sensor data from monitoring tens of thousands of assets can generate petabytes of data per year, and that can cost a company a fortune just to store the data.
WHY QUASARDB FOR ASSET HEALTH MONITORING?
1. Unlimited, real-time data ingestion and query
Extremely optimized for time-series data, QuasarDB has a track record of handling large data sets and delivering sub-second complex data analytics for sensor data while storing all the historical data.
2. No data lag
Quasar offers real-time, streaming data analytics, and a high-performance column-oriented query engine for time-series data, delivering powerful, immediate analytics. By receiving and storing data instantaneously from sensors, QuasarDB ensures that the database is always up-to-date with the latest information on asset conditions.
3. Data storage cost divided by 20
QuasarDB offers data compression, while leveraging object storage transparently, which leads to an additional cost reduction. Just by combining the two, QuasarDB helps companies crush data engineering costs in their asset health monitoring initiative, while still providing faster performance!
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