Re:Timescale raises $16M from Benchmark, NEA, Two Sigma Vent...
by wenc on Wednesday January 24, 2018 @12:23PM
(#16224402)
Except that most process historians store data in a non-relational way which makes it difficult to integrate with other types of data.
Process historians, like most NoSQL time-series offerings, are traditionally optimized for fast data retrieval (for HMIs, real-time monitoring dashboards, etc.) but are really weak when it comes to analytics. There are new products like Seeq (a kind of a Tableau for process historians) that attempt to address this, but somehow miss the fact that for analytics, you really need your data to exist in a dataframe-like shape.
Today, if you want to do any kind of non-trivial analytics, you typically have to first ETL data from the historian to a relational database via REST or OLE DB API. TimescaleDB on the other hand already stores data in a native relational format and lets you run full SQL on it.
Process historians, like most NoSQL time-series offerings, are traditionally optimized for fast data retrieval (for HMIs, real-time monitoring dashboards, etc.) but are really weak when it comes to analytics. There are new products like Seeq (a kind of a Tableau for process historians) that attempt to address this, but somehow miss the fact that for analytics, you really need your data to exist in a dataframe-like shape.
Today, if you want to do any kind of non-trivial analytics, you typically have to first ETL data from the historian to a relational database via REST or OLE DB API. TimescaleDB on the other hand already stores data in a native relational format and lets you run full SQL on it.