Preface

Over the past several decades, the world of data has evolved dramatically—from the structured era of relational databases to the expansive realms of big data and fast data. Today, we are entering a new phase: the age of deep and connected data. As data volumes grow and analytics become increasingly interdependent, traditional database systems are being reimagined. Graph technology has emerged as a powerful solution, offering new possibilities for modeling and querying complex relationships.

Before the standardization of Graph Query Language (GQL), the graph database landscape was fragmented. Popular query languages such as Cypher (Neo4j), Gremlin (Apache TinkerPop), GSQL (TigerGraph), UQL (Ultipa), and AQL (ArangoDB) each introduced unique features tailored to specific platforms. While these innovations advanced the field, they also created challenges for users—requiring time and effort to learn multiple proprietary syntaxes.

The introduction of GQL (ISO/IEC 39075) marks a pivotal moment in database history. As the second standardized database query language—following SQL’s release in 1986 (ANSI) and 1987 (ISO)—GQL provides a unified, vendor-neutral syntax for querying graph databases. This standardization fosters interoperability, reduces learning curves, and accelerates adoption across industries.

This book begins with the evolution of graph databases and query languages, setting the stage for a comprehensive understanding of GQL. You’ll explore its syntax, structure, data types, and clauses, and gain hands-on experience through practical examples. As you progress, you’ll learn how to write efficient queries, optimize performance, and apply GQL to real-world scenarios such as fraud detection.

By the end of this journey, you’ll have a solid grasp of GQL, be equipped to implement a graph-based solution with GQL, and gain insight into the future direction of graph technology and its growing role in data ecosystems.