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TigerGraph

Freemium
graph databasedata analyticsbig datamachine learningenterprisedatabasecloudgsqlfraud detectioncypher

A distributed native parallel graph database and analytics platform designed for enterprises to perform deep link analysis on massive datasets in real-time, uncovering critical insights from complex connected data.


TigerGraph is a high-performance graph analytics platform built for handling large-scale, complex, and interconnected data. It serves data scientists, architects, and developers in enterprises across finance, supply chain, and cybersecurity who need to understand relationships within their data. The platform's unique value proposition stems from its native parallel graph architecture, which allows it to execute deep link queries (3 to 10+ hops) in real-time. This capability enables advanced use cases like fraud detection, customer 360, supply chain optimization, and personalized recommendations at a scale that traditional databases struggle with. By using its own powerful, SQL-like language called GSQL, TigerGraph provides the speed and scalability necessary for modern machine learning and AI applications built on graph data.

Pros

  • Extreme performance and scalability due to its distributed, native parallel graph engine.
  • Real-time data loading and updates, allowing for analytics on fresh, operational data.
  • Powerful GSQL query language, which is Turing complete and similar to SQL, making it familiar for many developers.
  • Built-in library of graph algorithms and an integrated Machine Learning Workbench.
  • Supports multi-graph, allowing for logical data separation and secure access control within a single instance.

Cons

  • Steeper learning curve for its proprietary GSQL language compared to competitors using the openCypher standard.
  • The ecosystem of third-party tools and community support is smaller than more established graph databases like Neo4j.
  • Managing self-hosted enterprise deployments can be complex and resource-intensive.
  • The user interface (GraphStudio) can be less intuitive for complex query building and visualization compared to some alternatives.

Key features

  • Native Parallel Graph (NPG) architecture
  • GSQL: A Turing-complete, high-level query language similar to SQL
  • Real-time deep link analytics (queries with 3+ hops)
  • Distributed database for horizontal scalability
  • TigerGraph Cloud fully-managed service
  • ML Workbench for in-database machine learning
  • GraphStudio visual SDK for exploration and development
  • Built-in data compression to reduce storage footprint

Integrations

Apache KafkaApache SparkTableauPower BIpyTigerGraph (Python connector)Java, C++, and Go SDKsAmazon S3Azure Blob StorageDatabricksSnowflake

Target audience

Enterprise data scientists, data architects, developers, and business analysts working in sectors like financial services, e-commerce, healthcare, and manufacturing on complex, large-scale data problems.


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Founded

2012

Headquarters

Redwood City, USA

Pricing Tiers

Cloud Free

An always-free tier for learning and small projects. Includes one graph solution with up to 50GB of storage on shared infrastructure.

Free

Cloud Pay-As-You-Go

For production applications with pricing based on vCPU hours, storage, backup, and data transfer. Offers various instance sizes and dedicated compute.

Usage-based

Enterprise Edition

A self-managed solution for deployment in a private cloud or on-premise data center. Includes enterprise-grade security, high availability, and dedicated support.

Custom


Frequently Asked Questions


Top Alternatives to TigerGraph

Neo4j

Choose Neo4j for its mature ecosystem, larger community, and declarative openCypher query language, which is often considered easier to learn.

Amazon Neptune

Opt for Neptune if you are heavily invested in the AWS ecosystem and prefer a fully-managed cloud service with support for both Property Graph and RDF models.

ArangoDB

Consider ArangoDB if you need a multi-model database that natively supports graph, document, and key/value data models within a single engine and query language.

DataStax Enterprise Graph

Select DataStax if your organization is already built on Apache Cassandra and you need a graph layer that leverages its proven scalability and fault tolerance.

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