As we have been keep track of the graphic scene for a while, a few things have started to become visible. One, graph is here to stay. Second, there is still a way to go to make the benefits of graph databases and analytics widely available and available. Add to this a newly found timeliness, as connection leverage is where this technology shines and you have the background of today’s announcement of TigerGraph.
Graf is here to stay
Though graph databases have a history that goes back at least 20 years, it is only in the last few years that it began to come into limelight. The recognition that the way data points are connected can provide more insight and value than pure data volume seems to have hit at home. At the same time, graph technology has made progress, while the limitations of current relational databases when it comes to exploiting connections are now well understood.
This has led 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 it last year this space will see a compound annual growth of 100% year on year until 2022. Every single industry executive we’ve talked to seems to confirm this – 2019 has actually been a very good year.
TigerGraph is no exception. TigerGraph is a relative beginner in this space, which has come out of 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’s VP Marketing Marketing Minister Gaurav Deshpande.
TigerGraph was one of the first graphic database providers to announce a fully managed cloud service at the end of 2019. In a call with ZDNetDeshpande noted that although the cloud-based version of the platform has only been generally available for a while, it is seeing rapid uptake.
Over the past four months, TigerGraph notes that 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 unified trend – data, databases and users all go together.
Still, this is just one of the pieces of puzzle graphics database providers will have to solve. Being offered in the cloud can take care of the accessibility part, but what about accessibility? Not everyone is an expert on graph to begin with. Even for those who are, it would help to have some kind of equivalent to the well-established technology stack that comes with established relational databases.
Wide Accessibility and Availability: Cloud, no code, visual tools
This is where TigerGraph’s announcement comes into play. The first part of what TigerGraph links to version 3.0 of its platform does not seem very revolutionary, but we get the feeling that it will be appreciated by many: the ability to automatically migrate data from relational databases to TigerGraph without it being need to create a data pipeline or create and map to a new graph schema.
As seen in a demo released by TigerGraph, migration seems pretty painless. Deshpande commented that this was a feature that TigerGraph has been working on for a while, and now the time has finally come to release it. Initial customer feedback has also been pretty positive.
Although TigerGraph is not the only graph database provider that offers some way to import data, other settings often require an intermediate step, ie. export to CSV format. This adds to the process complexity and cost as opposed to what appears to be a fairly 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 mapping and scheduling rules apply. This may be more of a problem than it seems, especially for complex domains.
The clarity of perception and navigation as well as query performance is highly dependent on an appropriate graph data model. Depending on your domain, an out-of-the-box graph data model 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. Currently, however, users are aware of this and are ready to take action as needed. But that’s not all they might want to use TigerGraph’s visual SDI to. In general, visual environments are a big boost to developers’ availability and productivity, and graphics database vendors have added them to their arsenals as well.
However, TigerGraph 3.0 goes a step further. In an industry first, as far as we know, TigerGraph 3.0 introduces visual query capabilities to 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 is likely to attract some attention and go some way to mitigating one of the problems of graph databases. while efforts to produce a universally standardized graphical query language are underway, no code queries is an interesting option in itself.
Using Connections COVID-19 Times
TigerGraph 3.0 introduces several enhancements, namely support for distributed environments in its cloud, and custom indexing. The former means that graph printing across the globe can now be scaled up in a better way, while the latter means that users can speed up database performance for specific queries.
Last but not least is an initiative coming at a time when graph analysis could really help the community as a whole. As the spread ofhas reached a pandemic status, according to WHO, one of the most important aspects of fighting the virus is identifying contacts for each individual tested positive.
This essentially comes down to exploiting connections, as the name of the game is to identify people with whom COVID-19 positive cases have been in contact. The idea is to map potential upstream sources from which the virus may have been acquired while keeping an eye on potential downstream contacts to try to contain additional contamination.
This is exactly the type of analysis where the graph is lit. Mastercard, the Bill & Melinda Gates Foundation and Wellcome have launched an initiative to accelerate COVID-19 development and access to therapies. TigerGraph noted and would like to lend a helping hand to this and all other initiatives aimed at stopping the spread of and improving the treatment of coronavirus worldwide.
For this reason TigerGraph offers free use of Cloud and Enterprise Edition for applications that require massive data or high computational needs. Local, state, and federal agencies, businesses as well as non-profits can instantly use the free tier of the TigerGraph Cloud to load data and perform advanced analytics.
Graph algorithms can be helpful there. Eg. Can Community Detection identify clusters of virus infection, PageRank can identify super-spreading events, and the shortest path can 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 almost failed to get stranded in Europe because of the recently imposed travel ban. Maybe this served as motivation for TigerGraph, but in any case at times like these, everyone should chip in as much as they can.