¶ TigerGraph
¶ Database Technologies
¶ Introduction to TigerGraph: Stepping Into the World of Real-Time Graph Analytics
Every era of data technology tends to bring forward one system that transforms how we think about information. In the early days, relational databases shaped the foundation of structured storage. Later, distributed systems opened the door to massive scaling. Then came specialized databases that handled time-series, documents, key-value pairs, and wide-column workloads. In the middle of all this evolution, graph databases quietly became one of the most powerful tools for making sense of relationships—real relationships, the kind that power recommendations, fraud detection, supply-chain mapping, knowledge graphs, and more.
TigerGraph is one of the most important players in this space. It didn’t just enter the graph ecosystem as another store for connected data. Instead, it brought a bold promise: real-time graph analytics at a scale that could support modern enterprises. Instead of trade-offs between query depth and performance, TigerGraph set out to prove that deep-link analysis can be fast—fast enough to influence live applications and critical decisions.
This course of 100 articles is meant to introduce you to that world. But before we go deep into queries, architectures, and analytics techniques, it’s worth spending time with the bigger story—what TigerGraph is, why it matters, and how it fits into a world overflowing with data.
¶ Why Graph Databases Have Become Essential
To appreciate TigerGraph, you first need to understand the philosophy of graph technology. Traditional databases treat data as records—rows and columns, neatly arranged. This works extremely well when your questions are simple: “What is the balance of this account?” “How many items are in stock?” “What orders were placed this month?”
But modern data challenges often revolve around relationships. Who is connected to whom? Which transactions look similar to fraudulent patterns when traced through multiple layers? What chain of suppliers leads from a manufacturer all the way to a retail store? How does information flow through an organization?
Graph databases treat relationships not as a by-product but as a first-class citizen. They store data as nodes and edges, allowing them to model real-world connections in a way that mirrors how people, systems, and processes interact.
The rise of social networks, recommendation systems, cybersecurity analytics, and supply-chain intelligence has made graph databases not just useful but essential. And that’s the environment into which TigerGraph stepped—an environment hungry for depth, speed, and enterprise reliability.
¶ Where TigerGraph Stands Out
TigerGraph stands apart for several reasons. Many graph databases focus on flexibility, schema freedom, or ease of modeling. While these qualities are valuable, they often come with performance limitations when queries become deep or datasets become massive.
TigerGraph takes a different approach. It is built with a heavy emphasis on:
- Native graph processing that prioritizes traversal speed
- High concurrency to support real-time applications
- Parallel computation for handling large-scale analytics
- Ability to perform multi-hop queries instantly, even across billions of nodes
- Strong schema design, ensuring predictability and efficiency
- Integration with machine learning use cases where connected features matter
The idea is simple: graph analytics shouldn’t feel slow. You shouldn’t have to wait several seconds—or minutes—just to compute a recommendation or detect a suspicious pattern. TigerGraph’s architecture aims to minimize these delays so organizations can act in real time.
¶ A Look at TigerGraph’s Philosophy
If you trace the principles behind TigerGraph’s design, a few emerge clearly:
-
Speed is not optional
The founders believed that if graph databases were to power real-time systems, they had to be built for speed from the ground up. Parallelism, optimized storage, and efficient traversal algorithms are central to the engine. -
Deep analytics must be natural, not forced
Many graph engines struggle when queries go beyond a few hops. TigerGraph treats deep-link analytics as a core capability. -
Enterprise workloads demand reliability
It offers high availability, clustering, backup systems, and operational tooling because real-world systems need more than just fast queries—they need stability. -
Scalability must be linear and predictable
As datasets grow, performance should remain consistent. TigerGraph’s architecture supports horizontal scaling without degrading query times.
These ideas shape everything you will learn in this course, from storage architecture to query optimization.
¶ Why TigerGraph Is Gaining Momentum
In recent years, TigerGraph has started showing up in more enterprise discussions, research papers, tech conferences, and analytics platforms. The reason is rooted in the challenges businesses face today.
Think about the following scenarios:
- A bank trying to identify a fraud ring by analyzing connections across thousands of accounts, devices, merchants, and locations.
- An e-commerce platform aiming to deliver personalized recommendations instantly as a user browses.
- A telecom provider wanting to trace call patterns to detect anomalies or prevent churn.
- A logistics company mapping supply-chain relationships across regions for resilience planning.
- A healthcare organization analyzing medical knowledge graphs for clinical insights.
Each of these scenarios depends heavily on relationships. They require systems that can search, connect, compare, and compute instantly, often while receiving new data continuously. TigerGraph’s ability to handle both transactional and analytical graph workloads makes it uniquely positioned for such applications.
¶ A Database That Encourages Modeling Connected Reality
One of the most interesting aspects of working with TigerGraph is how naturally it pushes you to think in terms of connections. Every dataset that initially looks like a set of records suddenly reveals layers of relationships you may have missed:
- Customers connected through shared purchase behaviors
- Products linked by attributes, categories, and user interactions
- Fraud attempts connected through identity overlap
- Supply chains connected through transport routes
- Knowledge graphs connected through semantic relationships
Once you begin modeling with TigerGraph, you start seeing your data more holistically. The database does not force you to translate real-world relationships into awkward table structures. Instead, it mirrors reality itself.
¶ What Learning TigerGraph Feels Like
At first glance, TigerGraph may look unfamiliar, especially if your background is in relational databases. The query language—GSQL—has its own personality. The architecture introduces new layers, such as graph partitions, accumulators, and distributed execution mechanisms. But once you get comfortable, the system begins to feel remarkably intuitive.
You’ll learn concepts like:
- Vertices and edges as the core building blocks of graph modeling
- Directed and undirected relationships and how they shape queries
- Graph partitions to distribute data across nodes
- GSQL queries that express deep traversal logic cleanly
- Query performance tuning using accumulators, hints, and execution profiling
- Graph algorithms that reveal insights hidden in the data
- Integration patterns such as REST APIs, Kafka streams, and Spark pipelines
Perhaps the most appealing part is that GSQL allows you to write queries that feel close to how you describe relationships in natural language. Instead of forcing yourself into the rigid shapes of joins and subqueries, you can walk across connections in a way that feels fluid.
¶ The Roles Surrounding TigerGraph
People often assume that graph databases are used only by data scientists or specialized engineers, but TigerGraph supports a broad range of roles:
- Data engineers use it to store and process large volumes of connected data.
- Application developers build recommendation engines, fraud detection tools, and graph-powered apps.
- Data scientists extract graph features for machine learning models.
- Business analysts rely on connected insights to inform decisions.
- Database administrators manage clusters, implement backups, and optimize performance.
One of TigerGraph’s strengths is its flexibility. It can serve as the backend for real-time applications, a tool for data science experiments, or a powerhouse for analytical systems that rely on deep graph computation.
¶ The Growing Importance of Real-Time Insight
In today’s world, the pace at which decisions need to be made has changed dramatically. Waiting hours for a batch process to identify suspicious activities or generate recommendations is no longer practical. Businesses compete on speed as much as on accuracy.
TigerGraph plays directly into this need for real-time analytics. Its engine is designed to deliver:
- Multi-hop queries within milliseconds
- Real-time updates and ingestion
- Streaming integration for continuous data flows
- Interactive graph exploration for analysts
- Instant scoring for machine learning applications
This shift—from reactive analysis to proactive intelligence—is one of the reasons TigerGraph has become a central topic in graph technology discussions.
¶ Where TigerGraph Fits in the Database Universe
The database ecosystem today is a rich landscape full of specialized technologies. TigerGraph fits into the category of native parallel graph databases, but its influence extends much further. It overlaps with:
- Analytics systems through its ability to process large graph computations
- OLTP systems when used for real-time queries in applications
- Machine learning platforms through graph feature extraction
- ETL and data integration pipelines via connectors and APIs
This versatility means that TigerGraph often becomes part of an organization’s larger data fabric, interacting with warehouses, lakes, streams, and application backends.
¶ What You’ll Gain from This Course
This course of 100 articles is designed to give you a deep, confident understanding of TigerGraph—from fundamentals to advanced-level thinking. Whether you're a beginner or an experienced engineer, you will gradually build a strong sense of how graph databases work and how TigerGraph brings its own unique approach to the landscape.
By the end, you will be able to:
- Model real-world systems as native graphs
- Write efficient and expressive GSQL queries
- Understand execution plans and performance characteristics
- Handle ingestion, indexing, partitioning, and scaling
- Apply graph algorithms for analytics and machine learning
- Build real-time applications powered by graph insights
- Maintain and monitor TigerGraph clusters
- Integrate TigerGraph with other systems in modern data architectures
The goal isn’t just to teach you the mechanics of using the database—it’s to help you understand how graph thinking can transform the way you approach data problems.
¶ A Journey into Connected Intelligence
TigerGraph represents more than a database engine. It symbolizes a shift toward understanding data as a living network of relationships. Whether you’re analyzing financial transactions, mapping knowledge, optimizing supply chains, or building recommendation engines, the insights you gain often depend on how well you can navigate and understand connections.
This course will take you through that journey step by step. You’ll see how graph models reveal hidden patterns, how real-time traversal empowers critical systems, and how TigerGraph’s architecture makes this possible even at massive scale.
By the time you complete all 100 articles, TigerGraph will no longer feel like a specialized tool. It will feel like a natural extension of how you think about and work with data—connected, dynamic, real-time, and full of insights waiting to be discovered.
¶ TigerGraph
¶ Beginner
1. Introduction to TigerGraph: A Powerful Graph Database
2. Getting Started with TigerGraph: Installation and Setup
3. Understanding Graph Theory: Nodes, Edges, and Properties
4. The TigerGraph Architecture: Components and Overview
5. Basic TigerGraph Terminology: Graphs, Vertices, and Edges
6. Exploring TigerGraph's GSQL Query Language
7. Creating and Managing Graphs in TigerGraph
8. Inserting Data into TigerGraph: Nodes and Edges
9. Understanding Vertex Types and Edge Types in TigerGraph
10. Basic Data Retrieval: Using GSQL SELECT Statements
11. Data Modeling for Graphs: Designing Nodes and Edges
12. Working with Graph Schema in TigerGraph
13. Using TigerGraph’s Visual Graph Explorer
14. Introduction to TigerGraph’s Analytics Capabilities
15. Graph Traversals in TigerGraph: Basic Concepts
16. Basic Graph Algorithms: PageRank, Shortest Path, etc.
17. Working with Graph Properties in TigerGraph
18. Importing Data into TigerGraph: Batch Loading and Streaming
19. Security Basics in TigerGraph: Authentication and Roles
20. Using TigerGraph's REST API for Basic Operations
21. Backup and Restore Procedures in TigerGraph
22. Understanding the TigerGraph Dashboard for Monitoring
23. Basic Troubleshooting in TigerGraph
24. Scaling a Single Node TigerGraph Deployment
25. Using TigerGraph for Social Network Analysis
¶ Intermediate
26. Understanding TigerGraph's Distributed Architecture
27. Cluster Setup and Configuration in TigerGraph
28. Advanced Graph Modeling: One-to-Many, Many-to-Many Relationships
29. Working with Edge Properties in TigerGraph
30. Handling Large-Scale Data in TigerGraph
31. Query Optimization in TigerGraph: Tips and Best Practices
32. Working with TigerGraph’s Graph Studio for Development
33. Building Graph Views and Subgraphs in TigerGraph
34. Advanced Data Loading in TigerGraph: Real-Time Streaming
35. Graph Traversals: Depth First vs. Breadth First Search
36. Creating and Running Advanced GSQL Queries
37. Using Aggregations and Filtering in GSQL
38. Advanced Graph Algorithms in TigerGraph
39. Graph Data Analytics in TigerGraph: Pattern Matching and Analysis
40. Setting Up TigerGraph for High Availability
41. Using TigerGraph for Fraud Detection and Prevention
42. Designing a Graph Database for IoT Use Cases
43. Security in TigerGraph: Encryption and Access Control
44. Leveraging TigerGraph’s Built-In Machine Learning Algorithms
45. Integrating TigerGraph with External Tools (e.g., Spark, Kafka)
46. Working with TigerGraph's Data Import API
47. Querying Graph Data with GSQL’s JOIN and Path Queries
48. Advanced Graph Algorithms: Community Detection and Clustering
49. Implementing Real-Time Recommendations in TigerGraph
50. Using TigerGraph for Knowledge Graphs and Semantic Networks
51. Optimizing Data Schema for Better Query Performance
52. Graph Visualization and Reporting with TigerGraph
53. Designing Distributed Graph Models in TigerGraph
54. Handling Data Consistency in Distributed TigerGraph Clusters
55. Implementing Temporal Data and Time-Based Queries in TigerGraph
56. Leveraging TigerGraph for Supply Chain Optimization
57. Understanding and Using TigerGraph’s Analytics Library
58. Graph Machine Learning: Node Classification and Link Prediction
59. Data Integrity in TigerGraph: Constraints and Validation
60. Scaling Graph Databases with TigerGraph: Horizontal Scaling
61. Building and Running Complex Graph Algorithms in TigerGraph
62. Setting Up Automated Backups and Snapshots in TigerGraph
63. Monitoring and Managing TigerGraph Clusters at Scale
64. Using TigerGraph’s RESTful API for Advanced Data Operations
65. TigerGraph for Real-Time Social Media Analytics
66. Optimizing Graph Queries for Large Datasets in TigerGraph
67. Building an E-commerce Recommendation System with TigerGraph
68. Using TigerGraph for Network Security and Threat Detection
69. Configuring TigerGraph for Multi-Region and Multi-Cloud Environments
70. Handling Schema Changes in Production TigerGraph Databases
71. Integrating TigerGraph with BI Tools for Analytics
72. Graph Analytics on Historical Data in TigerGraph
73. Understanding TigerGraph’s Query Execution Plan
74. Graph Data Modeling Best Practices in TigerGraph
75. Implementing Security Best Practices in TigerGraph
¶ Advanced
76. Designing a Global TigerGraph Cluster: Multi-Region Setup
77. Advanced Graph Querying: Recursive Queries and Pathfinding
78. Performance Tuning and Query Optimization in TigerGraph
79. Implementing Custom Graph Algorithms in TigerGraph
80. Building Graph Applications with TigerGraph's API
81. Optimizing TigerGraph for Large-Scale Machine Learning
82. Managing Large Graphs: Distributed Storage and Data Partitioning
83. Advanced Use Cases for TigerGraph: Supply Chains, Fraud, etc.
84. Graph Parallelism and Distributed Processing in TigerGraph
85. Building a Real-Time Graph Analytics Dashboard with TigerGraph
86. Deploying and Managing TigerGraph on Kubernetes
87. Advanced Graph Modeling for Complex Relationships
88. Building Predictive Analytics Models in TigerGraph
89. Integrating TigerGraph with TensorFlow for Deep Learning
90. Optimizing Storage and Memory Usage in TigerGraph
91. Designing for Fault Tolerance and Disaster Recovery in TigerGraph
92. Implementing and Running Large-Scale Data Pipelines in TigerGraph
93. Understanding TigerGraph’s Resource Management and Load Balancing
94. Extending TigerGraph with Custom User Functions (UDFs)
95. Graph Streaming and Real-Time Data Processing in TigerGraph
96. Building and Deploying TigerGraph GraphQL APIs
97. Leveraging TigerGraph for Genealogy and Ancestry Graphs
98. Building Advanced Network Graphs for Telecom and Utilities
99. Designing and Analyzing Complex Graphs for Smart Cities
100. The Future of Graph Databases: Trends, Challenges, and Opportunities in TigerGraph