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Summary

Graph database vendor, TigerGraph, hopes to democratize graph analysis. With the general availability of its newest flagship release (version 3.0) scheduled within the next two to three weeks, TigerGraph will introduce several drag-and-drop development features designed to bring graph analysis to non-technical business users.

Do you do graph analysis?

Graph analysis, or the analysis of relationships between entities such as friends on a social network, has rapidly garnered interest across the enterprise data management and analytics marketplace, owing to its innate capacity to tackle some very tough data-centric problems such as detecting fraud, identifying legitimate users (access control), and content/service recommendations. This sounds well and good, but compared with traditional relational database management systems (RDBMS), graph databases require a specific skill set.

To use TigerGraph and other graph databases such as Neo4j, ArangoDB, OrientDB, and others, would-be data analysts must throw aside traditional and highly standardized query languages such as SQL in favor of highly specialized counterparts. For market leader Neo4j, this language is Cypher. For TigerGraph, it’s GSQL. Fortunately, both languages build on top of the SQL standard, often simply extending the “FROM” query command and adding in additional, completely non-SQL features. Regardless, this form of technical debt stands as an impediment to would-be graph database practitioners. And it certainly prevents business owners and other non-technical stakeholders from realizing the value of this burgeoning area of analysis. For example, a data privacy compliance officer could use a graph database to perform ad hoc regulatory compliance research.

Enter TigerGraph 3.0. Though very much a first step for TigerGraph in democratizing graph analysis, the vendor is setting an early example for the rest of the market. Version 3.0, for instance, features Visual Query Builder, a no-code tool where users can simply draw the patterns (relationships) they want to understand. Users won’t need to understand the underlying schema or the structure of a proper query string. Instead, in graph database parlance, they will simply need to identify and connect nodes (people) using edges (friend, manager, for example).

Certainly, to differentiate opposite other pure plays such as Neo4j, TigerGraph can and should aggressively extend this capability using natural language processing (NLP) for both the query building and the result interpretation. This would enable users to state something as simple as “show me all the people who are friends with both me and my boss.” Otherwise, TigerGraph’s new builder will simply sit alongside similar efforts as with Neo4j’s Visual Cypher.

TigerGraph is also building out a set of Cloud Starter Kits. These pre-built solution accelerators combine pre-built schemas, queries, and sample data, with each kit targeting a specific graph-centric challenge such as building a 360-degree customer view. These, coupled with the company’s free tier on TigerGraph Cloud and Visual Query Builder, should speed proof-of-concept development and further fill the company’s potential customer pipeline.

Appendix

Further reading

“2020 Trends to Watch: Analytics and Data Management,” INT002-000272 (February 2020)

“2020 Trends to Watch: Customer Engagement,” INT001-000168 (December 2019)

“On the Radar: TigerGraph scales out the graph database,” INT002-000184 (October 2018)

Author

Bradley Shimmin, Distinguished Analyst, Data Management and Analytics

[email protected]

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