title: Advantages of using Spark with Cassandra
body: <p>I have seen that the combination of using Spark with Cassandra is relatively popular.</p>
  
  <p>I know that Cassandra is a BigData solution that provides reliability over consistency, therefore fits for real time systems. It also provides an SQL-like syntax for queries, but under the hood manages its data very differently than a normal DB.</p>
  
  <p>Hadoop on the other hand provides consistency over reliability, therefore fits for analytics systems. Its interface is MapReduce, which is quite slow and too low level for nowadays. So this is where Sparks comes in. Sparks uses Hadoop's HDFS and replaces the old MapReduce with better architecture that takes more advantage of memory rather than hard disk, and exposes better interfaces such as RDD and dataframes.</p>
  
  <p>So my question is:
  Why would I want to use Spark combined with Cassandra? What are the advantages of that? Why not to use just one of them?</p>
  
  <p>As far as I understand, Cassandra would just replace the HDFS, so I'd have reliability over consistency, and I'd also have to use RDD/dataframes instead of CQL, and spark would generate CQL under the hood, which gives me fewer control.</p>