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.

DSE Analytics provides automatic Spark Master management.

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.

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.

DSE Analytics Solo datacenters are devoted entirely to DSE Analytics processing, for deployments that require separation of analytics jobs from transactional data.

DSE Analytics uses the advanced security features of DSE, simplifying configuration and deployment.

AlwaysOn SQL is a highly-available service that provides JDBC and ODBC interfaces to applications accessing DSE Analytics data.