TigerGraph enhances fundamentals in latest platform update

The latest TigerGraph Cloud update offers a number of fundamental features, including a simplified setup for ingesting streaming data and improved support for development operations.

Based in Redwood City, California, TigerGraph is a database provider whose tools are based on graph technology.

Unlike traditional relational databases, where only data points can be connected to one other data point at a time, graph technology allows data points to be connected to multiple other data points at the same time. The result is a neural network that often allows users to discover relationships between data points more quickly and easily, resulting in faster query times and reducing the speed of insight.

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New Skills

TigerGraph first launched a cloud version of its platform in 2019. Subsequent updates focused on machine learning and connectivity to enable the development of a data ecosystem. In the meantime, the vendor has been working with other graph database specialists to develop a common query language and has worked towards the goal of making graph technology more mainstream.

Released on March 1st, version 3.9 of TigerGraph Cloud represents a return to basics for the vendor, according to Constellation Research analyst Doug Henschen.

In addition to simplified ingestion of streaming data and improved support for DevOps, which adds access to detailed operational information and the ability to monitor individual queries, the update includes:

  • Support for the open source Parquet data format;
  • advanced Kubernetes functionality;
  • improved self-service visualization capabilities that include collaborative editing on shared dashboards
  • Support for multiple edges of the same type to simplify time series forecasting and other types of analysis; And
  • an extended data science suite to enable more scalable embedding of pre-built algorithms.
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“I see many [fundamentals] for database developers, database administrators and DevOps types,” said Henschen. “[The update is] a hot list of feature requests and the closing of technical gaps.”

In 2022, TigerGraph’s roadmap included adding starter kits for various vertical applications, including fraud detection and customer experience, he continued.

But TigerGraph Cloud 3.9 provides a small pivot for more fundamental functionality.

“The shift suggests that customer and buyer feedback has prioritized greater maturation of the core database platform,” said Henschen. “Perhaps with all the recession hype and buyer caution, TigerGraph felt it was a good time to go back to basics and ensure it satisfied existing customers with feature and functionality requests.

According to Jay Yu, the vendor’s vice president of product and innovation, customer feedback was the primary driver behind the development of the features included in the latest TigerGraph Cloud update.

“New features are a direct result of customer requests and feedback,” he said. “For example, Parquet support is growing in popularity because it is becoming the de facto standard for data lakes where our customers want to ingest large amounts of data from their data lake into TigerGraph to reap the benefits [massively parallel processing] Architecture and Machine Learning of Graphs.”

Yu added that multi-edge support allows users to capture graph development, data ingestion is becoming a priority as customers provide larger and larger datasets on TigerGraph, and self-service analysis remains a crucial tool to enable more than just data professionals to work with data.

what’s ahead

With TigerGraph Cloud 3.9 now generally available, future updates will focus on four key areas, Yu said: enterprise readiness, performance and scalability, ease of use, and innovation.

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Enterprise readiness includes efforts to better optimize cloud capabilities, the addition of software development kits for graphics applications and vertical solutions, improved data observation tools, and enhanced data security features.

Performance and scalability improvements focus on making TigerGraph even faster.

Usability will include further work on a standard graph query language, further improving data integration tools and adding more no-code/low-code visualization capabilities.

And innovation will focus on augmented intelligence and ML capabilities, including natural language queries.

Henschen, meanwhile, noted that the cost of data management and analysis is a concern, especially since so many companies are laying off staff to cut costs. As such, he said he’d like to see TigerGraph — and other vendors — add more tools to help users analyze spending and do more to help customers keep their cloud computing costs under control.

“In line with the times, I see a great demand for cost analysis, cost controls, cloud spend transparency and new cost optimization programs,” said Henschen. “I see a lot of cloud and database vendors rolling out new capabilities along these lines.”

Eric Avidon is Senior News Writer for TechTarget Editorial and a journalist with more than 25 years of experience. He deals with analytics and data management.


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