DataStax

DSE Graph is a property graph that is fully integrated with DSE. In fact, it relies on DSE (and Cassandra underneath it) as a data store. It also integrates with a number of built-in DSE capabilities, including DSE Search and DSE Analytics. In addition, it is highly scalable and performant, 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.

Figure 2 – DataStax Studio

Moreover, DSE Graph 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. Moreover, for the purposes of OLAP, Gremlin (part of Tinkerpop), SQL and Spark APIs are supported, the latter including both batch and streaming. 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 automatic workload management is available.

DSE Graph includes 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 DSE Graph might be the DataStax Studio, 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 DSE Graph).

In general, the reason graph databases are worth caring about is that they perform well compared to more traditional databases for processing data that involves multiple, complex relationships. However, a graph database by itself can only do so much. In order to effectively address so-called graph problems, your graph must be embedded inside a full software stack that supports a wide range of capabilities, such as search, analytics, and so on. In other words, graph problems are bigger than just the graph database. This is where DataStax, with DSE and DSE Graph, excels, providing not only the graph database, but the full stack as well. Moreover, many of the benefits of DSE are carried over to DSE Graph – including continuous availability, hybrid-cloud deployment, scalability, and so on – and the two are well integrated, allowing DSE Graph to take advantage of a variety of capabilities that are available in DSE, including DSE Search and DSE Analytics. Moreover, DSE Graph in and of itself boasts some significant differentiators. This includes its dual processing engines, allowing you to easily switch between transactional and analytical processing, and DataStax Studio, a particularly impressive example of a visual development environment for graph.

The Bottom Line

Together with its parent platform, DSE Graph provides a complete and effective means of addressing graph problems, regardless of whether they are transactional or analytical in nature. If you already use DSE, DSE Graph makes for an excellent addition. If not, it provides a very good reason for doing so.