title: Using Dataflow vs. Cloud Composer
body: <p>I'd like to get some clarification on whether Cloud Dataflow or Cloud Composer is the right tool for the job, and I wasn't clear from the Google Documentation.</p>
  
  <p>Currently, I'm using Cloud Dataflow to read a non-standard csv file -- do some basic processing -- and load it into BigQuery.</p>
  
  <p>Let me give a very basic example:</p>
  
  <pre><code># file.csv
  type\x01date
  house\x0112/27/1982
  car\x0111/9/1889
  </code></pre>
  
  <p>From this file we detect the schema and create a BigQuery table, something like this:</p>
  
  <pre><code>`table`
  type (STRING)
  date (DATE)
  </code></pre>
  
  <p>And, we also format our data to insert (in python) into BigQuery:</p>
  
  <pre><code>DATA = [
      ("house", "1982-12-27"),
      ("car", "1889-9-11")
  ]
  </code></pre>
  
  <p>This is a vast simplification of what's going on, but this is how we're currently using Cloud Dataflow.</p>
  
  <p>My question then is, where does <code>Cloud Composer</code> come into the picture? What additional features could it provide on the above? In other words, why would it be used "on top of" Cloud Dataflow?</p>