M4A

TigerGraph

Scalable enterprise graph database for real-time analytics

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Memory Types

semantic, graph, relational

Integrations

python, java, kafka, spark


Overview


TigerGraph is an enterprise graph database designed for real-time graph analytics at massive scale. Unlike traditional graph databases focused on transactional workloads, TigerGraph excels at complex analytical queries across very large graphs. The platform is used by enterprises for fraud detection, recommendation systems, supply chain optimization, and AI/ML applications.


TigerGraph's native parallel graph processing engine can analyze billions of edges in real-time, making it suitable for applications requiring deep graph analytics. The platform uses GSQL, a SQL-like language for graph queries, making it more accessible to developers familiar with relational databases.


Key Features


  • **Native Parallel Processing**: Analyze massive graphs in real-time
  • **GSQL**: SQL-like graph query language
  • **Deep Link Analytics**: Multi-hop pattern matching
  • **Real-Time Updates**: Concurrent read/write at scale
  • **Graph Algorithms**: 60+ built-in algorithms
  • **ML Integration**: Train ML models on graph features
  • **Distributed Architecture**: Horizontal scaling
  • **Cloud Native**: Kubernetes-based deployment

    • When to Use TigerGraph


    TigerGraph is ideal for:

  • Large-scale fraud detection and security
  • Real-time recommendation engines at scale
  • Supply chain and logistics optimization
  • Financial network analysis
  • Telecom and network analytics
  • AI applications with graph feature engineering
  • Enterprise knowledge graphs with heavy analytics

    • Pros


  • Excellent performance for large-scale analytics
  • Real-time processing capabilities
  • GSQL easier for SQL developers
  • Strong enterprise features
  • Good for fraud detection use cases
  • Handles very large graphs well
  • Native parallel processing
  • Good visualization tools

    • Cons


  • More expensive than Neo4j
  • Smaller community and ecosystem
  • Less documentation than Neo4j
  • GSQL different from Cypher
  • Steeper learning curve for some use cases
  • Primarily enterprise-focused
  • Free tier is quite limited
  • May be overkill for simple graphs

    • Pricing


  • **Free Tier**: 50GB on cloud
  • **Enterprise**: Custom pricing
  • **TigerGraph Cloud**: Usage-based pricing
  • **On-Premise**: Contact sales

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