Waveform Data


Data volume

Waveform data from a sensor contains 144,000x more data points compared to regular data. Most infrastructures are incapable of managing the pressure.

Data lag

Data warehouses or data lakes can easily create hidden data lag (and cost?). Making them unable to deliver timely, actionable insights for applications like predictive maintenance.

Data analytics​

Waveform data requires specialized analytical techniques, such as Fourier transforms or computationally costly wavelet analysis, making real-time analytics more challenging. ​


1. Storage cost divided by 60

Thanks to the industry-proven, patented compression algorithms, QuasarDB compresses the data faster than it arrives, significantly reducing disk usage while enabling fast queries.

2. No data lag

Achieved through a real-time AI engine, QuasarDB has a track record of handling large data sets and delivering sub-second complex data analytics for waveform data. This real-time capability empowers data scientists to identify typical patterns before a problem arises.

3. High-resolution data increases prediction accuracy

With the help of unlimited ingestion capabilities, QuasarDB can capture full-resolution waveform data in real-time, instead of data with a reduced sampling rate. This unique capability dramatically increases the prediction accuracy.

4. Real-time data analytics

In addition to data compression and unlimited ingestion capabilities, QuasarDB also offers real-time data analytics during the ingestion and query processes, which leads to significantly increased efficiency and reduced data storage.

5. Quick deployment

QuasarDB supports SQL queries, a native Python interface, and all the standards your business depends on, which makes it easier for industrial engineers and analysts to access and analyze data without requiring specialized programming skills.

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