TigerGraph

Figure 2 – Logical architecture for AML

TigerGraph is about real-time analytics for anomaly detection, pattern recognition, IoT applications, making recommendations (next best offer) and similar environments where low latency is required. As an example of its use, Figure 2 illustrates the logical architecture deployed for anti-money laundering (AML) at one of TigerGraph’s users. The blue brackets indicate sub-graphs. Note the support for machine learning, both supervised and unsupervised. Note too the fact that this sort of application requires support for real-time processing of operational data, not just analytics.

You can access the database via GSQL. As its name suggests, this is “SQL like”. In addition, a migration toolkit is provided to port queries from Cypher into GSQL, allowing you to easily reuse queries written for Neo4j. The company is planning to add support for Gremlin, part of the Apache Tinkerpop project, in a future release. TigerGraph provides its own graph visualisation capabilities, and also offers a browser-based capability called GraphStudio that can be used to create graph models, queries and so forth. This has been built on top of GSQL to make the environment more user-friendly, allowing ad hoc exploration of your data. There is also a GSQL software developer’s kit (SDK) that third-party graph specialists could use to integrate with TigerGraph. In addition, there is a RESTful API capability, which means that it should be relatively easy to integrate with third-party tools such as Tableau. A user extensible library of graph algorithms is also provided. Several algorithms (such as PageRank) are available out of the box, with more forthcoming as development continues.

TigerGraph also supports machine learning via the ability to generate training data en masse, which can then be extracted into your machine learning solution and used to train models. The training data itself is derived from your graph, and can be exported into your machine learning solution on a continuous basis, for example, every two hours. Due to the complexity of the graph structure on which it is based, this exposes a large quantity of information (particularly relationships) which can then be analysed deeply for connections and patterns. This can dramatically improve the accuracy of your models, particularly compared to simple analysis of relatively uncomplicated training data.

The key point about TigerGraph is its performance. Most other graph databases were built originally to support operational environments and were not intended to be used for complex large-scale and real-time analytics, though they may have been extended in that direction since they were originally designed. TigerGraph, on the other hand, was designed specifically for these environments and it is therefore not surprising that benchmarks suggest that TigerGraph outperforms leading rival products.

The Bottom Line

We should emphasise “complex, large-scale and real-time” as well as “analytics” from the previous section. Add in the ability to process operational data in real-time and you should understand where and why TigerGraph has significant advantages.

Fig 02 TigerGraph analytics platform

Figure 2 shows the logical architecture of TigerGraph, though it doesn’t illustrate the many partitioning and replication options available, such as the fact that you can run multiple graph engines, with each engine hosting identical graphs with different partitioning algorithms tailored for different types of application queries. The front-end server will route application queries to the relevant engines based on the query type.

TigerGraph supports single sign-on, LDAP and Active Directory, encryption (both in motion and at rest) and role-based access control. There is also a collaboration service whereby multiple groups can share a single master database, with each having their own view into the database. This has important implications for compliance (not least GDPR) because this service allows you to manage and monitor data access, data lineage and personal data. This includes where a point of data was first acquired, whether consent was given in obtaining it, where it moved over time, where it resides in each system, and how it gets used. The product features one-click deployment to several major cloud marketplaces, including AWS and Microsoft Azure; supports Docker and Kubernetes containers; and includes direct integration with a number of popular data storage systems, as shown in Figure 2.

You can access the database via GSQL. As its name suggests, this is “SQL like”. The company is also planning to add Cypher and Gremlin migration kits in a future release. TigerGraph provides its own graph visualisation capability as part of a browser-based GraphStudio that can be used to create graph models, queries and so forth. This has been built on top of GSQL to make the environment more user friendly, allowing ad hoc exploration of your data. There is also a GSQL software developer’s kit (SDK) that third party graph specialists could use to integrate with TigerGraph. In addition, there is a RESTful API capability, which means that it should be relatively easy to integrate with third party tools such as Tableau. A user extensible library of graph algorithms is also provided. Several algorithms (such as Shortest Path, Similarity, PageRank, Label Propagation, Centrality, and Community Detection) are available out of the box, with more forthcoming as development continues.

Most databases targeted at supporting hybrid environments that need real-time analytics in conjunction with transaction processing require either that you store the data twice (once in row format and then again in columnar format) and/or they work on the basis of supporting “multi-temperature” data with “hot”, “warm” and “cold” data stored separately. Apart from any other considerations, one of the big advantages of graph databases in supporting these hybrid environments is that you only need to store the data once and you don’t need to worry about the temperature of the data if you don’t want to. This represents a significant saving in both costs and complexity. Of course, these benefits are generic to graph technology and not specific to TigerGraph. However, as we have stated, TigerGraph is rare amongst graph database products – especially property graph databases – in targeting real-time analytics.

The Bottom Line

TigerGraph focuses on complex, large-scale and real-time analytics while also supporting transaction processing through its ACID compliance. With the underlying benefits of using a graph-based approach, this makes TigerGraph a serious contender for hybrid processing environments.