Key Benefits:

600x ROI according to Forrester 2022.
Deeper insights on live data via in-database graph analytics and customizable algorithms
40x to 300x faster graph queries, according to independent benchmarks

Businesses who want to get more value out of their connected data turn to TigerGraph. Need easy-to-use kits and low/no-code tools? Need a flexible graph query language, APIs, and customizable algorithm library to empower your developers? Need a scalable platform for your data scientists to apply the latest graph-enhanced analytics/ML/AI technologies? TigerGraph has it.

TigerGraph Lite is for developers who want to try TigerGraph with the full control and privileges of a self-managed deployment, with the convenience of a machine image on the Azure Marketplace. If you are ready to run a TigerGraph cluster, for scale-out or high availability replication, please contact TigerGraph Sales sales@tigergraph.com. If you're looking for a SaaS platform, check out TigerGraph Cloud on the Azure Marketplace.

What Makes TigerGraph Unique?

Built for performance and scale: native graph in C++ with distributed, massively-parallel architecture
Handles both real-time transactions and complex analytics on the same distributed database
GSQL language - SQL-inspired, high-level, supports pattern matching and analytics, for maximum developer productivity

Ease of Use:

GraphStudio: visual SDK for developer productivity: modeling, loading, querying, exploring, visualizing
Built-in connectors for ultra-fast, parallel ingestion from cloud object storage, Snowflake, Spark, Kafka, and more

ML and Analytics:

More accurate predictions via ML Workbench add-on for graph-powered ML
pyTigerGraph Python driver

Note: TigerGraph Solution Kits and Insights are currently only available on TigerGraph Cloud.

TigerGraph Lite for Faster & Better InsightsTigerGraph4.8(11 評分)

TigerGraph Lite for Faster & Better Insights

TigerGraph

TigerGraph Lite for Faster & Better Insights

TigerGraph

TigerGraph Lite for Faster & Better Insights
TigerGraph
4.8(11 評分)