Meet Yu Xu, a 2022 Datanami Particular person to Watch


    Graph databases are one of many quickest rising applied sciences in huge information immediately, and one of many quickest rising graph database distributors is TigerGraph, which is headed by Yu Xu, one in all Datanami‘s Individuals to Look ahead to 2022.

    TigerGraph founder and CEO Yu Xu isn’t any stranger to the challenges of constructing distributed computational engines. After getting his PhD in distributed databases from UC San Diego, he headed up the street to Teradata, the place he led the MPP (massively parallel processing) database workforce. Then Xu headed off to Twitter, the place he helped constructed the social media firm’s distributed information infrastructure.

    In 2017, Xu based TigerGraph, which has grown into one of many main suppliers of graph databases. Earlier this 12 months, Xu discovered time to reply a number of questions from Datanami about his firm and being named a Particular person to Look ahead to 2022:

    Datanami: Scale and efficiency have been TigerGraph’s calling playing cards because the firm burst upon the graph database scene a number of years in the past. Are these traits nonetheless resonating with prospects immediately?

    Xu: Sure. Enterprises proceed to build up extra information and need to achieve deeper perception from their information. Scale and efficiency for superior analytics are nonetheless critically vital for enterprises to make well timed and higher knowledgeable enterprise choices.

    Graph databases have been round for years. What’s stopping organizations from utilizing them extra extensively?

    Graph momentum is little doubt accelerating. Gartner predicts that 80% of enterprises will use graph databases in 2025, a 7X progress. Up to now, earlier generations of graph databases didn’t scale to huge datasets or carry out for superior analytics.

    It is a huge cause why firms aren’t utilizing graphs extensively. For instance, many TigerGraph prospects – reminiscent of UnitedHealth Group and a number of the largest banks – weren’t new to graph. They’d been utilizing graph options for fairly some time earlier than TigerGraph. The distinction? TigerGraph enabled them to ingest their greatest datasets to get the utmost question efficiency wanted (that was in any other case unattainable with earlier generations of graph databases).

    Since TigerGraph launched out of stealth about three years in the past, we now have been serving to such prospects to show their PoCs/ demos to manufacturing, and enabling them to leverage the complete advantages of graph for extra use instances, throughout bigger groups. These prospects have gained monumental enterprise worth.

    One other factor could be the shortage of standardization of a graph question language. A graph database is probably the most highly effective database (when it comes to expressiveness) which additionally means graph question languages are versatile and have superior options not out there in different database languages.

    Lack of standardization slows down graph adoption, however that is going to vary quickly! ISO, which standardized SQL for RDBMS about 40 years in the past, goes to launch a global graph language named GQL in about 18 months. My workforce at TigerGraph has been working with different firms on the ISO committees to verify GQL is highly effective, straightforward to make use of, and just like SQL. We’re excited to share extra within the coming months.

    What do you hope to see from the graph information group within the coming 12 months?

    We’re seeing thrilling progressions in relation to utilizing {hardware} to speed up graph analytics, particularly because it pertains to methods graph algorithms are intensively computing to unleash deeper insights. TigerGraph is working intently with Xilinx and Intel on {hardware} accelerated graph analytics. We hope to see extra improvements on this area.

    Moreover, it’s no secret that graph augments present AI and machine studying options effectively. In actual fact, as many as 50% of Gartner shopper inquiries across the matter of AI contain a dialogue round using graph know-how.

    Within the coming 12 months, TigerGraph will launch extra graph-AI options and information science libraries. Our hope is that extra information scientists will leverage the ability of graph of their initiatives.

    To learn the remainder of our interviews with Datanami Individuals to Look ahead to 2022, click on right here.


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