In any manufacturing facility, breakdowns are expensive and disruptive. They can result in wasted material and extensive damage to the assets.
It is possible to anticipate these breakdowns, perform root cause analysis, and create a virtuous circle to reduce their occurrence with complete monitoring.
To provide insights about plant operations, one needs to collect, analyze, and visualize numerous metrics.
These metrics allow having a clear understanding of breakdowns, the meantime to respond and repair the breakdowns, and ultimately anticipate them.
However, a plant with many assets generates tens of millions of records per second. This volume exceeds the capabilities of a typical relational database, a default choice for many of these setups.
As volume increases, updates are longer and longer, sometimes taking hours.
When connecting the data to, for example, Power BI, queries are sluggish, and the data lag prevents the monitoring from being effective.
Quasar can collect all events as they come and store them efficiently. The collection is direct and does not require any third-party software.
Quasar has no practical limit to how many records per second can be inserted and can thus handle hundreds of millions of updates per second at low latency.
Quasar will use its unique algorithm to compress the data faster than it arrives and use its micro-indexes to reduce future lookup and feature extraction time. This results in significantly reduced disk usage without impacting the queryability of the data.
Each sensor is stored in a dedicated table storing timeseries data. Tables can be tagged at will, enabling a flexing querying mechanism based on those tags. For example, obtaining all the sensors of an asset or in a specific area of the plant.
Microsoft Power BI, or any similar tool, can connect to Quasar directly using ODBC, and queries are instant, delivering sub-minute end-to-end data freshness and enabling operational intelligence.