Love your application data platform
The most popular open source Graph database in the world
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Loved by data engineers & developers
The Modern Graph Database
Built to be fault-tolerant, on a distributed graph database, that gives developers the tools to rapidly build and model applications at scale
Easy to use
Simply create one GraphQL schema, deploy it, and have instant database and API access.
Query the way you want. Choose industry-standard GraphQL for speed and simplicity, and extend with the more powerful DQL language when you need to.
High performant
Queries are broken into sub-queries, which run concurrently to achieve low-latency and high throughput.
Learn MoreLimitless scale & fault tolerant
Automatically shards graph and rebalances across as many nodes as you need.
Delivers the highest level of service reliability due to replicated data and redundant query processing capability.
Single Application, Database Schema, and Language
Other vendors
The Dgraph Way
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Learn more about GraphQL with Dgraph
To immediately see what Dgraph is about, check out the GraphQL and/or DQL tour.
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Modern Applications Build on a
Graph Data Model
Graph Data Model is designed for both small and large records with complex interconnections within and across (without joins) records.
Rapid Application Development
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.
Recommendation Engine
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.
Entity Resolution
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.
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Knowledge Graphs
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.
Customer Ordering Journey
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.
Process Monitoring
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.
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Latest from our blog
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Rapid application development with Dgraph data platform
This blog post reviews the steps needed to rapidly set up a GraphQL-based application factory. The instructions and sample code cover how to model and think about the data, set up the Dgraph database for GraphQL storage and API access, load data, and integrate it into React components to build a web app.
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GraphQL atop a Graph Database
GraphQL atop a Graph Database When I first learned about GraphQL I was confused - at least by the name. The name “GraphQL” suggested a query language to access graphs, but everyone was using it to dynamically query relational and other data.
Dynamic AutoScaling of GitHub Runners
In this article we explain our transition to GitHub Actions for our CI/CD needs at Dgraph Labs Inc. As a part of this effort we have built (in-house) & implemented a new architecture for “Dynamic AutoScaling of GitHub Runners” to power this setup.
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