Build an entire application in native GraphQL with the app server and database built into one. Eliminate the need for data mapping back and forth from GraphQL to the database schema, resulting in improved performance, greater scalability, and maintainability.

Learn how to compute similarity functions with graph traversal and computation along the path. No complex matrix extraction needed. Store computed similarity as additional knowledge in the graph for efficient real-time recommendation engine in any business.

Identify and match real-world entities in unstructured data sources. Dgraph’s ability to store, link, and traverse nodes enables data standardization, duplicate detection, and data merging. Enabling entity discovery and matching in a scalable and efficient manner while improving data quality.

Learn how to use Dgraph to build a knowledge graph in order to organize and make sense of large amounts of data,  represent knowledge as a network of entities and their relationships and provide a rich and flexible way of modeling complex domains.

Learn how to design a customer ordering journey using GraphQL and align with concepts from the Domain Driven Design technique. With Dgraph, you get out of the box support for GraphQL APIs and modeling techniques that support rapid iteration.

Detect complex fraud involving multiple entities and accounts with Dgraph.  Unlike traditional relational joins, Dgraph enables analyzing large amounts of data to identify crucial relationships and patterns, resulting in more accurate and effective fraud detection.