2. Smart Data Handling
To work faster and more efficiently you want to be able to monitor and respond to data in real-time, regardless of the data volume. Depending on the type of measurement, the duration and sample frequency, an overwhelming avalanche of data will hit you.
Your challenge ahead is not only to collect the data, but to store and analyze it in the most reliable way. To reach this goal you will need a solution that offers a faster and more efficient handling of large data streams.
One way a smart data backend can operate with is for example to distinguish between hot and cold data and to handle those types differently. Raw data and data that is less-frequently accessed and only needed for auditing or test post-processing (“cold data”) is stored in a distributed streaming platform that scales extremely efficiently. If you have to store, process and calculate new variables from hundreds of thousands of samples per second and from hundreds of channels at the same time, this distributed streaming architecture will show its strength and power.
So-called “hot data”, measurement data that must be accessed immediately for analysis, is provided in a time series database. This database stores data securely in redundant, fault-tolerant clusters. All measurement data is automatically backed up. Flexible data aggregation ensures that measurement data is continuous processed from the streaming platform to the database at predefined sample rates.
However, the same data can be replayed and stored at a higher sample rate in case detailed analysis around an unexpected event or specimen failure is required. This approach minimizes the investment cost for IT and storage infrastructure in the test lab, whilst maintaining the necessary computing performance for test-critical data analysis tasks.
No matter the exact solution, to guarantee your smooth surf on any data avalanche ahead you will rely on a smart data backend, that contains services for connectivity and is adaptable and scalable for high-performance edge computing services.
As a sweet side benefit a distributed and scalable data backend offers even more control over your cost-performance ratio as you can access your test setup and data from anywhere around the globe. Your engineering team might not be in one place or your biggest client will need support quickly, no matter the time zone: All of this can be easily provided through scalable data backends.