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TigerGraph

Master TigerGraph from scratch and learn to build dynamic, data-driven graph applications using GSQL with practical deployment strategies.
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Course Duration: 10 Hours
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TigerGraph – Master Graph Databases and GSQL Query Language – Online Course
 
TigerGraph Master Graph Databases and GSQL Query Language is a comprehensive, self-paced online course meticulously designed to equip aspiring data professionals, data scientists, software engineers, and tech enthusiasts with the indispensable skills needed to navigate and master the powerful TigerGraph database system. This course transforms beginners into proficient practitioners, capable of utilizing TigerGraph for a wide array of data analytics, real-time decision-making, and complex relationship analysis tasks, from fraud detection to recommendation engines and supply chain optimization. Whether you are taking your first steps into the intricate world of graph databases or looking to significantly enhance your existing skill set with a robust, enterprise-grade tool, this course offers an unparalleled foundation, extensive practical experience, and precise, step-by-step guidance for building and deploying real-world graph solutions.
 
At its core, TigerGraph is a native parallel graph database designed for real-time deep link analytics on massive datasets. Unlike traditional relational databases or even other NoSQL databases, TigerGraph's unique massively parallel processing (MPP) architecture allows it to perform complex traversals and aggregations on billions of vertices and edges in milliseconds. This unparalleled performance and scalability, combined with its powerful, declarative query language GSQL, makes it an ideal choice for applications that require understanding intricate relationships and patterns within data. This course leverages that advantage by starting with the basics of graph theory and steadily progressing to more advanced TigerGraph-specific tools and methodologies, all while maintaining an engaging, project-driven learning curve. This approach ensures that every concept learned is immediately applicable and reinforces a practical understanding of how real-world graph solutions are designed and deployed.
 
Through an in-depth, hands-on approach, this course covers everything necessary to become proficient in TigerGraph and its vast ecosystem of tools. You will begin with the fundamentals – setting up your TigerGraph environment (including TigerGraph Cloud and local installations), understanding graph schema design, and basic GSQL commands for data manipulation. As you progress, you will delve into advanced GSQL querying for complex pattern matching, aggregations, and sub-queries, explore data loading strategies, implement real-time analytics, and even learn about user-defined functions (UDFs) and stored procedures. Each module is designed to provide practical experience with TigerGraph Studio, gsql shell, and integrating TigerGraph with external applications. By the end of this course, you will not only be familiar with these tools but will also understand the underlying principles of graph data modeling and analytics, enabling you to apply your knowledge to real-world scenarios.
 
The unique aspect of this course lies in its practical, scenario-based learning methodology. We don't just teach you how to use a tool; we teach you why and when to use it, and how to interpret the results for maximum business value. You will work through numerous simulated scenarios, ranging from building a social network graph to detecting fraudulent transactions and optimizing supply chains in a controlled lab environment. This hands-on experience is crucial for developing the critical thinking and problem-solving skills essential for a successful career in data science and graph analytics. Furthermore, the course emphasizes understanding the performance implications of different graph designs and query patterns, promoting the development of highly efficient and scalable solutions.
 
You will gain a deep understanding of graph theory, common graph algorithms (like PageRank, Community Detection, Shortest Path), and how to implement them efficiently in TigerGraph using GSQL. The curriculum is meticulously crafted to ensure that you gain proficiency in crucial areas such as real-time analytics, data ingestion pipelines, and deploying graph solutions for production use. Beyond the technical aspects, the course also touches upon architectural considerations for integrating TigerGraph into existing enterprise data ecosystems.
 
By the end of the course, you will have built multiple real-world projects and conducted comprehensive simulated graph database implementations. These aren't just exercises; they are portfolio-ready engagements that showcase your ability to design, implement, and query complex graph datasets using TigerGraph. You will learn how to:
  • Set up and configure your TigerGraph environment securely for development and deployment.
  • Design optimal graph schemas to represent complex relationships and data.
  • Master the GSQL Query Language for all aspects of graph data manipulation and analysis.
  • Perform deep link analytics to uncover hidden patterns and insights within interconnected data.
  • Implement common graph algorithms (e.g., shortest path, community detection, PageRank) using GSQL.
  • Develop real-time analytics solutions for immediate decision-making.
  • Efficiently load and manage large datasets within TigerGraph.
  • Integrate TigerGraph with external applications using REST APIs and client connectors.
  • Optimize GSQL queries for maximum performance and scalability.
  • Generate meaningful insights from graph data to solve real-world business problems.
This course goes beyond simply teaching you the tools; it helps you understand the entire lifecycle of a graph database project and how real-world data science and analytics operations work. Whether your goal is to become a graph data scientist, a backend engineer specializing in graph solutions, enhance your data analytics skills, or simply understand how complex relationships are leveraged to generate insights, this course is the definitive gateway to achieving those ambitions.
 
What You Will Gain
 
By the end of the course, you will have conducted multiple real-world simulated engagements, such as:
  • A fraud detection system identifying suspicious patterns in financial transactions.
  • A recommendation engine suggesting products or connections based on user behavior and relationships.
  • A social network analysis platform to understand community structures and influence.
  • A supply chain optimization model to visualize and analyze dependencies and potential bottlenecks.
These projects aren't just for practice—they serve as portfolio-ready engagements that showcase your ability to design, implement, and analyze complex graph datasets using TigerGraph.
 
But this course goes beyond using tools—it helps you understand how real-world graph database solutions work. You'll learn how to:
  • Model complex relationships effectively using graph structures.
  • Write powerful GSQL queries for deep link analytics.
  • Implement and customize graph algorithms for various use cases.
  • Optimize graph database performance for real-time applications.
  • Integrate graph insights into existing data pipelines and applications.
  • Develop professional graph solution designs with actionable recommendations.
Who This Course Is For
 
This course is perfect for:
  • Aspiring Graph Data Scientists who want to specialize in powerful relationship analysis.
  • Data Engineers and Architects looking to incorporate graph databases into their data ecosystems.
  • Backend Developers building applications that leverage highly connected data.
  • Students and Beginners in database technologies looking for a structured and approachable course in graph databases.
  • Tech Professionals aiming to understand advanced data modeling and analytics.
  • Entrepreneurs and Freelancers who want to build data-driven applications with deep insights.
Regardless of your starting point, the course is structured to take you from zero to confidently building and deploying graph solutions with clarity and confidence.
 
How to Use This Course Effectively
 
To maximize your learning and apply your skills effectively, follow these tips for using the course:
  1. Follow the Sequence The course is designed to build progressively on knowledge. Start from the first module and move forward in order. Each concept introduces new techniques while reinforcing previously learned skills. Skipping ahead may cause confusion later, especially in projects that require cumulative understanding.
  2. Build Alongside the Instructor Hands-on practice is essential. As you watch the video tutorials, execute the GSQL commands and use TigerGraph Studio in your own TigerGraph environment (TigerGraph Cloud or local Docker/VM setup recommended). Don’t just observe—type the commands yourself, experiment with variations, and troubleshoot errors. This repetition will solidify your learning and build real-world problem-solving skills.
  3. Use the Projects as Practice and Portfolio Pieces Each project you build during the course has real-world value. Customize them, add your own features, and consider documenting your solutions for a portfolio. These projects can become part of your portfolio when applying for data science or engineering jobs.
  4. Take Notes and Bookmark Key Concepts Keep a graph database journal. Write down important GSQL syntax, schema design patterns, algorithm implementations, and lessons learned. Bookmark the modules covering key concepts like data modeling, complex querying, or performance optimization for quick reference.
  5. Utilize the Community and Support Resources If the course offers a discussion forum, Slack group, or Q&A section, use it! Ask questions when you're stuck and help others when you can. Participating in a community will deepen your understanding and expose you to diverse perspectives and solutions.
  6. Explore TigerGraph Documentation and GSQL Reference TigerGraph has extensive official documentation. You’re encouraged to explore it further, especially the GSQL language reference. Developing the habit of reading official documentation will make you a more independent and resourceful graph database professional.
  7. Practice Different Data Loading Strategies Data ingestion is a critical part of working with graph databases. Experiment with various data loading methods covered in the course (e.g., LOAD CSV, REST API) and understand their implications for different data volumes and update frequencies.
  8. Review and Revisit Graph database development is a skill built through repetition and iteration. Don’t be afraid to revisit previous lessons or re-implement a graph solution from scratch. Each time you do, you’ll catch something new or improve your understanding and efficiency.
Why Learn TigerGraph?
 
TigerGraph is a leading enterprise-grade native parallel graph database known for its unparalleled performance, real-time deep link analytics capabilities, and powerful GSQL query language. It's trusted by large enterprises across various industries for mission-critical applications requiring real-time insights from highly connected data, such as fraud detection, customer 360, supply chain optimization, and AI/ML applications. Learning TigerGraph gives you direct access to a cutting-edge technology and teaches you valuable concepts applicable to any role in data science, data engineering, or advanced analytics. This course not only teaches you TigerGraph—it empowers you to build real-world graph applications, derive actionable insights from complex relationships, and contribute to data-driven innovation. It’s practical, engaging, and career-oriented. Whether you're learning TigerGraph for a job, a personal project, or to enhance your organization's data capabilities, this course provides the foundation and confidence to succeed. Start today, and begin building the skills to design, implement, and manage your own graph solutions.

Course Objectives Back to Top
By the end of this course, you will be able to:
  1. Understand the fundamental architecture and components of the TigerGraph database.
  2. Design effective graph schemas using nodes, edges, attributes, and types for various real-world scenarios.
  3. Master the GSQL Query Language for all data definition language (DDL) and data manipulation language (DML) operations.
  4. Write complex GSQL queries for advanced pattern matching, aggregation, and multi-hop traversals.
  5. Implement and utilize built-in and custom graph algorithms for analytics such as PageRank, Community Detection, and Shortest Path.
  6. Perform efficient data loading using LOAD CSV and understand best practices for large datasets.
  7. Develop real-time analytics solutions and create executable stored procedures (queries) in TigerGraph.
  8. Interact with TigerGraph using TigerGraph Studio, gsql shell, and REST APIs.
  9. Optimize GSQL queries and graph schema designs for maximum performance and scalability.
  10. Integrate TigerGraph with external applications and visualization tools.
  11. Troubleshoot common issues and debug GSQL queries effectively.
  12. Articulate the value proposition of graph databases and TigerGraph for various business problems.
Course Syllabus Back to Top
TigerGraph Course Syllabus

 
Module 1: Introduction to Graph Databases and TigerGraph
  • What are Graph Databases? (Nodes, Edges, Properties)
  • Why Graph Databases? Use Cases and Advantages
  • Introduction to TigerGraph Architecture
  • Setting Up TigerGraph Environment (Cloud, Local Installation)
Module 2: Graph Schema Design
  • Principles of Good Graph Design
  • Defining Vertices and Edges
  • Adding Attributes to Vertices and Edges
  • Best Practices for Schema Modeling
Module 3: GSQL Fundamentals – Data Definition Language (DDL)
  • Creating Graphs and Schema
  • Adding/Dropping Vertices and Edges
  • Managing Attributes
  • Schema Evolution
Module 4: GSQL Fundamentals – Data Manipulation Language (DML)
  • Inserting Data with INSERT
  • Updating Data with UPSERT
  • Deleting Data with DELETE
  • Basic MATCH and SELECT Queries
Module 5: Advanced GSQL Querying - Pattern Matching and Traversal
  • Introduction to Pattern Matching
  • Multi-hop Traversals
  • Filtering with WHERE Clause
  • Aggregations (ACCUM, POST-ACCUM)
  • Sub-queries and Common Table Expressions (CTEs)
Module 6: Data Loading and Management
  • Loading Data with LOAD CSV
  • Loading Strategies for Large Datasets
  • Monitoring Data Loading
  • Managing Data Quality
Module 7: Real-time Analytics and Stored Procedures (Queries)
  • Writing GSQL Stored Procedures (Queries)
  • Executing Queries and Parameters
  • Developing Real-time Graph Analytics
  • Concurrency and Transactions
Module 8: Graph Algorithms in GSQL
  • Introduction to Graph Algorithms
  • Implementing PageRank
  • Community Detection (e.g., Connected Components, Louvain)
  • Shortest Path Algorithms (BFS, Dijkstra)
Module 9: Performance Optimization and Best Practices
  • Query Optimization Techniques
  • Index Management
  • Schema Optimization for Performance
  • Understanding Query Plans
Module 10: Integrating with TigerGraph
  • TigerGraph Studio Overview
  • Using the gsql Shell
  • REST API Interaction
  • Client Connectors (Python, Java - Overview)
Module 11: Security, Monitoring, and Administration (Conceptual)
  • User Management and Roles
  • Monitoring TigerGraph Performance
  • Backup and Restore Concepts
  • Scalability and High Availability
Module 12: Real-World Use Cases and Solutions
  • Fraud Detection and Financial Crime
  • Recommendation Engines
  • Customer 360
  • Supply Chain and Logistics
  • Knowledge Graphs
Modules 13–20: Project-Based Learning
  • Building a Social Network Graph and Analysis
  • Implementing a Fraud Detection System
  • Developing a Movie Recommendation Engine
  • Supply Chain Visibility and Optimization
  • Customer 360 View Application
  • Cybersecurity Threat Intelligence Graph
  • Knowledge Graph for Enterprise Data
  • Personalized Content Discovery Platform
Module 21: TigerGraph Interview Questions & Answers
  • Top Interview Questions for Graph Database Roles
  • Best Practices and Explanations
Certification Back to Top
Upon successful completion of the course, learners will receive an industry-recognized Certificate of Completion from Uplatz that validates their skills in TigerGraph and GSQL. This certification serves as a powerful addition to a resume or LinkedIn profile, demonstrating a candidate’s proficiency in graph data modeling, GSQL querying, and real-time graph analytics. It helps professionals stand out in job interviews and increases credibility when applying for roles such as Graph Data Scientist, Data Engineer, or Solution Architect specializing in graph databases. The certificate reflects both theoretical understanding and practical experience gained through hands-on projects, making learners job-ready.
Career & Jobs Back to Top
TigerGraph skills are in high demand in the rapidly growing field of graph databases and advanced analytics, especially among organizations seeking to unlock deeper insights from their connected data. Completing this course prepares learners for roles such as:
  • Graph Data Scientist
  • Data Engineer (Graph Focus)
  • Solution Architect (Graph Databases)
  • Business Intelligence Analyst (with Graph Expertise)
  • Backend Developer (Graph-enabled applications)
  • Fraud Analyst (Graph-based)
Professionals with TigerGraph skills can pursue job opportunities at tech companies, financial institutions, healthcare providers, retail giants, consultancies, and any organization dealing with complex, interconnected data.
Interview Questions Back to Top
1.      What is TigerGraph, and what are its key advantages over other database types? TigerGraph is a native parallel graph database designed for real-time deep link analytics on massive datasets. Its key advantages include its MPP (Massively Parallel Processing) architecture for unparalleled query performance, its powerful declarative GSQL query language, and its ability to handle complex, multi-hop traversals in real-time, making it ideal for connected data.
 
2.      Explain the core components of a TigerGraph graph schema. A TigerGraph graph schema consists of VERTEX types (nodes) and EDGE types (relationships). Both vertices and edges can have ATTRIBUTES (properties) that store data. Vertices have a PRIMARY_ID for unique identification. Edges connect two vertex types and can be UNDIRECTED or DIRECTED.
 
3.      What is GSQL, and how does it differ from SQL? GSQL (Graph Structured Query Language) is TigerGraph's native, declarative, and Turing-complete query language designed specifically for graph data. Unlike SQL, which is optimized for tabular data and joins, GSQL is optimized for graph traversals and pattern matching. It includes features like MATCH for pattern matching, ACCUM for local aggregations, and POST-ACCUM for global aggregations during traversals.
 
4.      Describe the process of loading data into TigerGraph using LOAD CSV. LOAD CSV is a GSQL command used to efficiently load data from CSV files into a TigerGraph graph. The process involves defining a LOADING JOB which maps columns from the CSV file to vertex and edge attributes in the defined graph schema, including specifying the primary ID for vertices and source/target vertices for edges.
 
5.      How do you perform a multi-hop traversal in GSQL? Provide a simple example. Multi-hop traversals in GSQL are performed using the FROM ... -(EDGE_TYPE)- (VERTEX_TYPE:N_HOPS)- ... syntax. For example, MATCH (p1:Person)-[:FRIENDS]-(p2:Person)-[:FRIENDS]-(p3:Person) WHERE p1.name == "Alice" RETURN p3 would find friends of Alice's friends (2-hop traversal).
 
6.      What are GSQL stored procedures (queries), and why are they important? GSQL stored procedures (often just called "queries" in TigerGraph) are pre-compiled and optimized GSQL queries that can be installed on the database and executed efficiently. They are important for real-time analytics, complex operations, and encapsulating business logic, providing significant performance benefits compared to ad-hoc queries.
 
7.      Explain the concept of ACCUM and POST-ACCUM in GSQL. ACCUM is used for accumulating values locally on each traversed vertex or edge during a traversal. POST-ACCUM is used for global aggregations or final calculations after the ACCUM phase completes for all traversed elements in a query. They are crucial for complex analytical queries.
 
8.      How would you approach designing a graph schema for a recommendation engine? For a recommendation engine, you might have User and Product vertices. Edges could include LIKES (User -> Product), BOUGHT (User -> Product), VIEWED (User -> Product), and SIMILAR_TO (Product -> Product). Attributes on edges could store ratings or timestamps. This schema allows for personalized recommendations based on explicit and implicit user behavior and product similarities.
 
9.      What are some common graph algorithms you can implement in TigerGraph, and what are their use cases? Common algorithms include:
 
o    PageRank: For ranking importance/influence (e.g., social network influencers, website popularity).
 
o    Community Detection (e.g., Louvain, Connected Components): For finding tightly knit groups or clusters within a network (e.g., identifying fraud rings, customer segments).
 
o    Shortest Path: For finding the most efficient route between two nodes (e.g., logistics, network routing).
 
o    Centrality Measures (Degree, Betweenness, Closeness): For identifying key nodes in a network.
 
10.  How would you optimize a slow GSQL query in TigerGraph? Optimization strategies include:
 
o    Reviewing the query plan: Use EXPLAIN to understand execution flow and identify bottlenecks.
 
o    Optimizing schema design: Ensure appropriate primary IDs, direct edge types, and minimal attributes for frequently accessed data.
 
o    Adding indexes: Create indexes on attributes used in WHERE clauses for faster filtering.
 
o    Batching operations: Use LOAD CSV or batch inserts for large data loads instead of individual insertions.
 
o    Pre-computing results: For frequently run analytics, consider creating a GSQL stored procedure.
 
o    Minimizing data transfer: Only return necessary attributes and avoid unnecessary joins.
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