
Fig 01 - Monitoring the DataStax DSE environment
The DSE Graph Engine is a property graph that is built into DSE and leverages DSE’s capabilities for storage, search and analytics. Consequently, it inherits the scalability, high availability, performance (as much as 10 times faster with this re-engineering) and real-time processing that Cassandra and DataStax are well known for, with scaling up to billions of entities. In service to this, it leverages optimisation techniques such as query optimisation, data partitioning, and distributed query execution, among others. In particular, now that the graph data model is within the platform, this means that you can store your data exactly once but access it via either Cassandra or Gremlin (part of Tinkerpop) APIs. This means, for example, that you can create CQL (Cassandra Query Language) tables and read them via Gremlin, or vice versa. Thus providing interoperability and transparency. SQL and Spark APIs are also supported, with the latter supporting streaming environments as well as batch processing.
The Graph Engine is designed for both transactional and analytical processing, and consequently features two processing engines – one transactional, one analytical – and allows for both OLTP and OLAP graph traversals. Furthermore, switching between engines (and therefore modes of traversal) is relatively simple, and can be done without altering the underlying data. This means that you can leverage transactional and analytic queries on a single set of data, as needed. In addition, analytical and transactional workloads are separated, and automated workload management is provided. Notable new features for graph processing include significantly faster and simpler loading processes (because you are now simply loading into Cassandra) and intelligent indexing tool that analyses the traversals that you regularly make and then recommends appropriate indexes in order to optimise traversal performance.

Fig 02 - DataStax Studio
There are a variety of tools for managing all aspects of your graphs and graph clusters. This includes Lifecycle Manager and OpsCenter, which allow you to automate and visualise the creation of new graph clusters, respectively. However, the most important tool for interacting with the Graph Engine is probably DataStax Studio (see Figure 2), a visual, browser-based development environment for your graph. It supports Spark SQL, Gremlin, and CQL (Cassandra Query Language), and additionally comes with a built-in smart Gremlin editor, similar to an RDBMS smart query editor. In fact, much of DataStax Studio is similar in feel to the visual development tools available in more conventional, relational environments. Moreover, to support the visualisation aspect of this tool, DataStax partners with a number of visualisation vendors, including Cambridge Intelligence, Tom Sawyer, Linkurious and Tableau (although the latter is a more general partnership, and not specific to graphs).