DSE Analytics

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

Use DSE Analytics to analyze huge databases. DSE Analytics provides real-time, streaming, and batch analytics with built-in integration with Apache Spark, a distributed, parallel data processing engine.

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.
All analytics keyspaces are initially created with the SimpleStrategy replication strategy and a replication factor (RF) of 1. Each of these must be updated in production environments to avoid data loss.