TigerGraph

Last Updated: 20th October 2023
Analyst Coverage: Philip Howard and Daniel Howard

TigerGraph is based in California and has been in existence since 2012 but was primarily in stealth mode until mid-2017. It has some prestigious users, including Visa, Uber, Citrix, and Alipay, amongst others that span a variety of industries including banking, media and entertainment, healthcare, automotive and retail and hospitality. Energy efficiency analytics and the Internet of Things are also major areas of focus. TigerGraph is VC-backed.

TigerGraph is a native graph parallel database that is available in both on-premises and cloud versions. The company also offers TigerGraph Cloud, which is provided as a managed service. There is a free trial program for enterprises and a free developer edition for non-commercial use. Also available is GraphStudio, which is a visual query builder, and TigerGraph Insights, a visual analytics tool native to the TigerGraph platform. The product features one-click deployment to several major cloud marketplaces, including AWS and Microsoft Azure; it supports Docker and Kubernetes containers; and includes direct integration with a number of popular data storage systems, including relational databases (Snowflake, Teradata et al), Hadoop, object storage and various types of file systems, as well as both Kafka and Spark.

Historically, the product’s most significant selling point has been its high performance within a graph context. More recently, TigerGraph has shifted its marketing towards more specific value propositions, with AI and machine learning a particularly notable focus. To this end, the company now offers a specialised Machine Learning Workbench for delivering graph-enhanced machine learning. TigerGraph is also able to act as a backend for various AI and machine learning technologies, such as PyTorch and Jupyter Notebook.

Company Info

Headquarters: 3 Twin Dolphin Dr., Suite 225, Redwood City California 94065, USA
Telephone: +1 650 206 8888

TigerGraph

Last Updated: 11th September 2020

What is it?

Fig 01 - Architecture overview

TigerGraph uses a property graph paradigm and has been designed specifically to support real-time (less than one second) analytics. The keys to achieving this are parallelism, compression and the way that, in TigerGraph, graph edges and vertices are not just units of storage but also computational units. The engine supports the processing of these in parallel, and the product also includes a parallel loader, as the product’s architecture, shown in Figure 1, illustrates. Compression can be more than 10x, according to TigerGraph, and compression is also used as a part of the loading and transformation processes, to further improve performance. Also relevant is the graph partitioning, which supports application-specific partitioning as well as mixed partitioning strategies. This is all handled automatically within TigerGraph Cloud. There is also the ability to 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.

Other significant features include security (single sign-on, support for LDAP and Active Directory, encryption – both in motion and at rest – and role-based access control); more than 20 starter kits for TigerGraph Cloud (examples include data lineage, financial services fraud detection, and in-database machine learning for real-time recommendations); user-defined indexing; and 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.

In the latest release (3.0) GraphStudio has been extended to provide a no-code migration capability from relational databases. At present this is limited to supporting PostgreSQL and MySQL, but this is likely to be extended. The company estimates that around 80% of the effort involved in migration will be automated through the use of this tool.

Customer Quotes

“We selected TigerGraph for its superior data warehousing speed and computational processing capacity, which improved performance by an order of magnitude.”
IceKredit

“Alipay streams 2B+ daily events in real time to a graph with 100B+ vertices and 600B+ edges on a cluster of only 20 commodity machines.”

What does it do?

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. It supports both supervised and unsupervised machine learning and a target market for the company is in leveraging its graph models to generate training data for machine learning purposes. TigerGraph also supports geolocation capabilities, which are important in many IoT and similar environments. However, it does not offer support for shape files and polygon processing, which is why we refer to it as supporting geolocation rather than geospatial capabilities.

Fig 02 - TigerGraph's visual query builder

You can access the database via GSQL. As its name suggests, this is “SQL like”. However, the company 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. Indeed, in the latest release TigerGraph has added a visual query builder – see Figure 2 – to GraphStudio, which means that anybody can build queries without having any knowledge of GSQL. We expect this to become the de facto standard method for working with TigerGraph.

In addition, a migration toolkit is provided to port queries from Cypher into GSQL, allowing you to easily reuse queries written in that language. There is also a GSQL software developer’s kit (SDK) that third party graph specialists could use to integrate with TigerGraph, and there is a RESTful API capability, which means that it should be relatively easy to integrate with third party tools such as Tableau. We would like to see the company supporting GraphQL as an alternative API. A user extensible library of graph algorithms is also provided. Several algorithms (such as PageRank) are available out of the box.

Why should you care?

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.

We are also particularly pleased by the introduction of the visual query builder, which should help to democratise the use of TigerGraph by providing self-service capabilities for business analysts and others that do not, and do not want, to understand GSQL.

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.

Commentary

A new direction for TigerGraph?

Solutions

TigerGraph

TigerGraph

Company Info

TigerGraph

What is it?

What does it do?

Why should you care?

Commentary

Solutions

Research

Graph Databases (2023)

Graph Database (2020)

TigerGraph (2020)

Hybrid real-time data processing

TigerGraph (June 2019)

Graph Database Market Update 2019

TigerGraph (January 2019)