About DSE Analytics 

Use DSE Analytics to analyze huge databases. DSE Analytics includes integration with Apache Spark.

DataStax Enterprise (DSE) integrates real-time and batch operational analytics capabilities with an enhanced version of Apache Spark™. With DSE Analytics you can easily generate ad-hoc reports, target customers with personalization, and process real-time streams of data. The analytics toolset lets you write code once and then use it for both real-time and batch workloads.

DSE Analytics jobs can use the DataStax Enterprise File System (DSEFS) to handle the large data sets typical of analytic processing. DSEFS replaces CFS (Cassandra File System).

DSE Analytics features 

DataStax Enterprise supports SparkR for R analytic processing.
No single point of failure
DSE Analytics supports a peer-to-peer, distributed cluster for running Spark jobs. Being peers, any node in the cluster can load data files, and any analytics node can assume the responsibilities of Spark Master.
Spark Master management
DSE Analytics provides automatic Spark Master management.
Analytics without ETL
Using DSE Analytics, you run Spark jobs directly against data in the database. You can perform real-time and analytics workloads at the same time without one workload affecting the performance of the other. Starting some cluster nodes as Analytics nodes and others as pure transactional real-time nodes automatically replicates data between nodes.
DataStax Enterprise file system (DSEFS)
DSEFS (DataStax Enterprise file system) is a fault-tolerant, general-purpose, distributed file system within DataStax Enterprise. It is designed for use cases that need to leverage a distributed file system for data ingestion, data staging, and state management for Spark Streaming applications (such as checkpointing or write-ahead logging). DSEFS is similar to HDFS, but avoids the deployment complexity and single point of failure typical of HDFS. DSEFS is HDFS-compatible and is designed to work in place of HDFS in Spark and other systems.