TigerGraph, provider of a leading graph analytics platform, is collaborating with Hewlett Packard Enterprise (HPE) and Xilinx, Inc. on a solution to make graph analytics capabilities more accessible for enterprises to accelerate insight while reducing costs and resources.
The bundled solution, which is comprised of HPE ProLiant servers using Xilinx accelerator cards and TigerGraph’s native parallel graph database provides more effective real time analytics for things like fraud detection, customer360, and supply chain optimization in manufacturing, according to the vendors.
Now any company can easily load, process, and analyze massive amounts of data in real-time to find key relationships within data and realize the full transformative potential of graph analytics.
“We’re excited to collaborate with HPE and Xilinx—both companies are renowned for constantly pushing the boundaries of technology, and the combined possibilities are endless,” said Dr. Jay Yu, vice president of product innovation, TigerGraph. “The joint solution enables companies to make discoveries that derive value from the vast amount of data within their organizations. The simplicity, elegance, and accessibility of the solution puts graph into the hands of any organization that wants to reap the full transformative potential of graph analytics.”
The TigerGraph-HPE-Xilinx joint solution helps organizations transform structured, semi-structured, and unstructured data within massive silos into an intelligent, interconnected, and operational data network that can reveal insights that support business goals.
Together, the companies are delivering faster, deeper, and wider insights on connected data using TigerGraph’s industry-leading graph analytics platform, HPE ProLiant servers that deliver industry-leading performance, security and versatility, and Xilinx’s blazing-fast Alveo accelerator cards. This combined solution enables businesses to achieve the following:
- Powerful problem solving – Solve business challenges that exceed the capabilities of traditional legacy relational databases, which are complex, slow, and perform poorly when it comes to deep analytics
- Better, faster queries and analytics – Higher performance for querying related data that enables more powerful insights to drive superior business outcomes
- Enhanced machine learning and AI – Enable amplified insights into non-obvious relationships through graph analytics and machine learning
- Simpler and more natural data modeling – Assign semantic meaning to represent relationships that help businesses understand their customers, resources, and risks with actionable insights
- Reduced infrastructure costs — Offloading memory-intensive graph algorithms to Alveo reduces RAM requirements by 67%
- Improved results quality — The faster time-to-insights allows for additional iterations, improving accuracy by 18%
“We are committed to empowering our customers to gain insight from their data to reach their business outcomes,” said Krista Satterthwaite, vice president and general manager, mainstream compute, compute business Group, HPE. “Together with TigerGraph and Xilinx, we have a powerful combined solution that offers every data-driven organization the ability to harness the power of graph technology to analyze and act on data to unlock value faster—available through the HPE GreenLake edge-to-cloud platform for an agile cloud experience with the security, governance, and visibility of on premise.”
For more information about this news, visit www.tigergraph.com.