title: Advice for long-term persisting using influx and redshift
body: <p>Our architecture consists long-term and short-term analytics. 
  Today we aggregate all our data using dropwizard and put it into Influx. Influx is easy to query as it's time-series based database.</p>
  
  <p>The thing is that "today's" data getting old and therefor keep it on MS(millisecond) sampling on influx is a waste. </p>
  
  <p>Influx introduced downsmapling feature where you can transfer ms's records into weeks/months, etc.. the problem is that we need to expend our queries and query from "downsampled" tables and non-downsampled queries.</p>
  
  <p>we thought about an idea where we can use short-term queries for influx and downsampled data to put on redshift as a long-term persistent data.</p>
  
  <p>what do you think? perhaps it's a waste or?</p>
  
  <p>as requested adding more details:
   how many rows of data: we expect around 1000 requests per second. each request would be a record
   how complex are the queries: pretty straight forward (e.g how many transactions in the last day/week/year, how many distinct users have logged in, etc..)</p>
  
  <p>how many concurrent users: pretty small. 50,100 </p>