Graph databases and analytics are getting ever more accessible and relevant
As we’ve been keeping track of the graph scene for a while now, a couple of things have started becoming apparent. One, graph is here to stay. Two, there’s still some way to go to make the benefits of graph databases and analytics widely available and accessible. Add to this a newly-found timeliness, as leveraging connections is where this technology shines, and you have the backdrop for today’s announcement by TigerGraph.
Graph is here to stay
Even though graph databases have a history that goes back at least 20 years, it’s only the last couple of years that it started getting in the limelight. The realization that the way data points are connected can bring more insights, and value, than sheer data volume seems to have hit home. At the same time, graph technology has been making progress, while the limitations of incumbent relational databases when it comes to leveraging connections are now well understood.
This has lead to a perfect storm for graph databases. Graph databases went from a niche market to the fastest-growing segment in data management in almost no time. Gartner, for example, predicted last year that this space will see compound annual growth of 100% on a year to year basis till 2022. Every single industry executive we’ve spoken to seems to verify this — 2019 has been a very good year indeed.
TigerGraph is no exception. TigerGraph is a relative newcomer in this space, having emerged from stealth in 2017. Before that, however, TigerGraph’s people have been working on their platform since 2012. This is starting to pay off, according to TigerGraph VP Marketing Gaurav Deshpande.
Leveraging connections is where graph databases shine
TigerGraph was one of the first graph database vendors to announce a fully managed cloud service in late 2019. In a call with ZDNet, Deshpande noted that even though the cloud-based version of the platform has only been generally available for a while, it is seeing rapid uptake.
During the past four months alone, TigerGraph notes, more than 1,000 developers have harnessed the power of graph to build applications on top of TigerGraph Cloud, the company’s graph database-as-a-service. This seems to be in line with the overall trend — data, databases, and users, are all going cloud.
Still, this is just one of the pieces of the puzzle graph database vendors will need to solve. Being on offer in the cloud may take care of the availability part, but what about accessibility? Not everyone is an expert in graph to boot with. Even for the ones who are, having some kind of equivalent for the well-established technology stack that comes with incumbent relational databases would help.
Wide availability and accessibility: Cloud, no code, visual tools
This is where TigerGraph’s announcement comes into play. The first part of what TigerGraph dubs version 3.0 of its platform does not seem particularly revolutionary, but we get the feeling it will be appreciated by many: the capability to automatically migrate data from relational databases to TigerGraph, without the need to build a data pipeline or create and map to a new graph schema.
As seen in a demo released by TigerGraph, the migration seems pretty painless indeed. Deshpande commented that this was a feature TigerGraph has been working on for a while, and now the time finally came to release it. Initial customer feedback has been pretty positive, too.
Although TigerGraph is not the only graph database vendor to offer some way of importing data, other options often require an intermediate step, i.e. exporting to CSV format. This adds complexity and cost to the process, as opposed to what seems like a pretty smooth import process for TigerGraph 3.0.
The flip side of this, however, is a lack of transparency and control. At this point, there is no way for users to control the process. This means that built-in rules for mapping and schema creation apply. This may be more of a problem than it seems, especially for complex domains.
The clarity in perception and navigation, as well as performance in querying, are very much dependent on an appropriate graph data model. Depending on your domain, an out-of-the-box graph data model may or may not be appropriate. Of course, it’s a start. As Deshpande pointed out, users can always intervene to fine tune their graph data model using TigerGraph’s visual IDE.
Over time, Deshpande said, the ability to control the process will be added. For the time being, however, users need to be aware of this and be ready to intervene as needed. But that’s not all they may want to use TigerGraph’s visual IDE for. Overall, visual environments are a great boost for developer accessibility and productivity, and graph database vendors have been adding those to their arsenals, too.
TigerGraph 3.0, however, goes one step further. In an industry first, to the best of our knowledge, TigerGraph 3.0 introduces visual querying capabilities for its IDE. In other words: users can now explore their graphs, and formulate and execute queries against the database, without actually learning TigerGraph’s query language or writing code.
This patent=pending capability will probably attract some attention and goes some way into mitigating one of the issues with graph databases. While efforts to produce a universally standardized graph query language are underway, no code querying is an interesting capability in its own right.
Leveraging connections in COVID-19 times
TigerGraph 3.0 introduces more improvements, namely support for distributed environments in its cloud, and user-defined indexing. The former means that graph deployments around the globe can now scale up in a better way, while the latter means that users can speed up the database performance for specific queries.
Last but not least, however, is an initiative that comes at a time when graph analytics could really help society at large. As the spread of the COVID-19 virus has reached a pandemic status, according to the WHO, one of the key aspects of tackling the virus is identifying contacts for every individual who has been tested positive.
This essentially comes down to leveraging connections, as the name of the game is to identify people with whom COVID-19 positive cases have been in touch. The idea is to pinpoint potential upstream sources the virus may have been acquired from while keeping an eye on potential downstream contacts to try and contain further contamination.
This is exactly the type of analytics where graph shines. Mastercard, Bill & Melinda Gates Foundation and Wellcome have launched an initiative to speed development and access to therapies for COVID-19. TigerGraph took note and would like to lend a helping hand for this and all other initiatives aimed at stopping the spread of and improving treatment for coronavirus worldwide.
For this reason, TigerGraph is offering free Cloud and Enterprise Edition use for applications requiring massive data or high computation needs. Local, State and Federal agencies, corporates as well as non-profit can immediately utilize the free tier on TigerGraph Cloud to load data and perform advanced analysis.
Graph algorithms may be of help there. For example, Community Detection can identify clusters of virus infection, PageRank can identify super-spreading events, and Shortest Path may help understand the origin and impact of spread in a particular area or community.
TigerGraph’s own founding team has roots in China, and some of its executives nearly escaped being stranded in Europe due to the recently imposed travel ban. Perhaps this served as motivation for TigerGraph, but in any case, at times like these, everyone should chip in as much as they can.