I can't comment on everything you posted, but I have a question and an 
observation:

*Question*: Is your temp/humidity & other data a stream?  In other words, 
do you have a constant "influx" of data being written to the tables?  

I only ask because a CQ need run only as frequently as the interval it 
summarizes.  The "Continuous" is as opposed to "ad hoc" (I believe you used 
the term "random") - they run automatically, without ceasing, on the 
prescribed interval.  For many who do have a stream of data (e.g. 
hundreds/thousands of records per second), running a CQ summarizing at the 
second is greatly beneficial.  The current data sets I have are not at the 
volumes I'm used to from my previous role, so my CQs run every minute, and 
ever hour (the two summary intervals).  

I hope that's clear and not assuming too much, but from your brief 
explanation, it seems like your CQ interval is way too frequent.

*Observation*:  Regarding CPU & number of tags - simply put, more tags = 
more expensive queries.

My first attempt through, I had a single node with 4 tag categories (one 
being a unique session ID for grouping records) and the cardinality was in 
the thousands.  After only a brief time, querying anything other than using 
the "where time > now() - 5m" would just result in the system hanging and 
eventually the query interface dying.

My solution was to drop two of the tags (cardinality is now ~100) and it 
runs very well. I realized that the analysis I wanted to do with those 
other tags was a better fit for Spark/Hadoop/EMR.

Hopefully this isn't reiterating something you've already gleaned, but I 
hoped my experience could shed some light on yours.

-- 
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