Upper right corner: TigerGraph received US$105 million in Series C financing, and the Graph market is growing

So far, the largest round of financing in the graphics market is not only good news for TigerGraph, but also for the entire market.
Author: George Anadiotis, Author: Big Data 2021 Nian 2 Yue 17 Ri -15: 08 GMT (23:08 SGT) | Topic: Big Data Analytics
Companies are good at collecting data, and the Internet of Things is taking it to a new level. However, the most advanced organizations are using it to drive digital transformation.
We are not really planning to review the graphics market. But sometimes news can hinder the development of the plan, and TigerGraphic announced that it has raised $105 million in Series C financing, which changed our plan.
TigerGraph is a graph database provider. We have been researching it since we withdrew from stealth in 2017. We regard the progress it has made in more than 3 years as the story of the entire graph. TigerGraph’s C series is led by Tiger Global, bringing TigerGraph’s total financing to more than $170 million.
This is the background of our conversation with TigerGraph CEO Yu Xu and COO Todd Blaschka. We discussed the evolution of TigerGraph and the evolution of the entire picture.
Our last contact with TigerGraph was about a year ago, when the COVID-19 crisis began. In a year-long period, TigerGraph has gone through a period of adjustment for many companies. Among them, due to the accelerating pace of digital transformation, data and analysis providers may even be at the top of the list in terms of results.
Xu said that for TigerGraph, this is how things are. The best quarter in the company’s history in 2020. Xu and Blaschka have dealt with different success stories. Clients include Intuit and Jaguar Land Rover to the Australian Taxation Office.
They also mentioned many use cases, from typical diagrams (such as customer 360 and supply chain analysis) to more unusual cases (such as blockchain analysis and tax anti-fraud). All is well, but there is almost one question to ask: Why do we need a round of financing?
To take this into account, there are a few things to consider. The picture generated by TigerGraph’s experience once again confirms our common insights with other suppliers in this field: they are moving from the database to the platform, closer to solving customer problems and creating value.
Graph has seen explosive growth, and TigerGraph’s funding is the largest so far in the field, which proves this.
Xu and Blaschka introduced how they see how to obtain a fast and scalable distributed graph database as a starting point. This allows them to gain a foothold in many organizations, even though they didn’t have much reputation or success stories to show at first. As Xu said, organizations “have no choice” but to use TigerGraph for certain types of use cases.
These use cases can be described as real-time graph analysis: obtaining answers that require real-time connection and traversal of many data sets (usually massive data sets). Xu said that in many cases, TigerGraph is the only choice for such use cases. Once adopted, customers also began to use it in other use cases, and today, TigerGraph is often used as the first solution for offline analysis, Xu continued to add.
Moving the TigerGraph’s stack up can be transformed into things such as adding visualization IDEs and query functions. This is something the company aims to develop further, and can be extended to areas such as what Xu calls “Graph Business Intelligence”. . Xu introduced in detail TigerGraph’s ambition to create “Tableau for Graph”. It is true that this ambition may require funding to promote it.
But this is not to say that TigerGraph has no down-to-earth operational aspects in its roadmap. TigerGraph has been running a database-as-a-service product for some time and supports AWS and Microsoft Azure. The company’s plans include increasing Google Cloud support and expanding its team to meet growing product demand, but there is more.
When discussing its cloud products, TigerGraph managers mentioned that they not only want to add Google Cloud support, but also want to add more features and better integration to its existing AWS and Microsoft Azure layers. When discussing what might be included, Xu emphasized that integration with machine learning libraries supported by cloud vendors is a good example.
Xu pointed out that taking Google’s BigQuery as an example, the integration of machine learning functions is being carried out in a wide range of data management platforms. The idea is simple-it can shorten the data pipeline required to process machine learning data. The purpose is to make the job of data engineers and data scientists easier.
Xu said the way to do this is by integrating machine learning-oriented extensions in SQL. TigerGraph has its own query language called GSQL, but this idea has been around for some time. In fact, graphics vendors need to do this for other reasons.
As we have already pointed out, Xu confirmed that graph-based machine learning is an area that has received widespread attention. In short, graph-based machine learning is about using multidimensional data and leveraging connections, rather than reducing everything to 2 dimensions. Therefore, it makes sense to use a graphics platform for this purpose.
When talking about graph query language, Xu also mentioned GQL. GQL is currently under the auspices of ISO, the standardization of the graphics query language, and has received the support of many suppliers. Since we haven’t received much news from this aspect for some time, we want to know what the situation is.
Xu is reassuring. He mentioned that GQL has made steady progress, and we may see results even before 2021. Like all standardization work, things tend to progress slowly. Considering how many people and suppliers are involved, this can be expected. Xu went on to add that this is the second query language standardized by ISO in 40 years after SQL.
Another point raised by Xu on GQL is that graphs are not like key-value databases or document databases. They do not have a standard query language and may not need this language. The graph is a richer and more complex data model, which is also richer than the relational model, and it does not make much sense to access it programmatically.
Does this mean that organizations are replacing them with graphical diagrams to replace their original relational databases? Not quite right yet, at least not yet, but it’s good. Xu mentioned TigerGraph as an example of the operation of the recording system, but mentioned that the focus is still on analysis. That said, however, more and more applications will be graphics first.
Author: George Anadiotis, Author: Big Data 2021 Nian 2 Yue 17 Ri -15: 08 GMT (23:08 SGT) | Topic: Big Data Analytics
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Post time: Mar-02-2021