No, it's not a typo. I didn't mean predictive. This morning I was reading an article from SparkCognition, "Changing the World by Way of Oil and Gas", and I came across the term for the first time. After some research, here is what I learned.
Why should you consider Chirp! for your next Ignition project? Ignition already offers some great SCADA and HMI solutions and their unlimited licensing model has resonated very well with system integrators and end users alike.
Could Cassandra be optimized to store time-series data? This is a question that has become a common topic for discussion. I recently came across a great read that compares Cassandra performance to another dedicated time-series database, TimescaleDB.
It's exciting to discuss the virtues of collecting and storing years of process data with engineers and operators. We often hear the ideas that are exchanged about what could be done and how the process could operate more efficiently if data was available to key individuals. It has struck me recently how many times these data historian discussions seem limited by the fear of budget and deployment time. Generally, by the end of a preliminary discussion, someone at the table sighs and makes a statement like, "It would be nice, but I'm sure the investment would be too much to consider".
Often users would like to add data values manually, especially when their organization still has a few pieces of equipment that are not able to have readings logged automatically. Most may not realize this is a free service that is included with all Canary Historian installations.
Add more analytic tools to the already powerful Ignition platform with Chirp! Powered by Canary's data historian, Chirp! works completely within Ignition and helps you:
"40 percent of the workforce at electric and gas utilities will be eligible for retirement in the next five years."
Recently, a system integrator asked us to provide data that would provide both speed and storage requirement comparisons between an out-of-the-box SQL database and Canary. They wanted to use this data to decide whether it would be best for customer's to deploy a Canary system, or deploy, tune, and maintain an SQL system.