Amazon Neptune vs Dgraph

Overview

Dgraph

Stacks124

Followers221

Votes9

GitHub Stars21.3K

Forks1.6K

Amazon Neptune

Stacks59

Followers174

Votes15

Amazon Neptune vs Dgraph: What are the differences?

Data Model: Amazon Neptune uses a property graph model, while Dgraph utilizes a graph database model with RDF (Resource Description Framework) triples, providing different ways to visualize and interact with data.
Query Language: Amazon Neptune supports Apache TinkerPop Gremlin and SPARQL for querying data, whereas Dgraph uses GraphQL±, a query language specifically designed for graph databases, offering a more user-friendly approach.
Scalability: Amazon Neptune is a fully managed service in AWS, providing automated scaling capabilities, while Dgraph is open-source and requires manual scaling to handle increased workloads, offering different scalability options.
Consistency Model: Amazon Neptune offers strong consistency, ensuring data integrity, while Dgraph provides eventual consistency by default, allowing for more flexibility in data replication and synchronization.
Community Support: Dgraph has a growing community with active contributors, forums, and documentation, fostering open collaboration and development, whereas Amazon Neptune has a more established support system being a service within AWS, offering enterprise-grade support options.
Advanced Features: Amazon Neptune includes features like multi-region replication, integrated security with AWS IAM, and automated backups, providing advanced functionalities for enterprise use cases, while Dgraph focuses on features like distributed transactions, sharding for horizontal scaling, and ACID compliance.

In Summary, the key differences between Amazon Neptune and Dgraph lie in their data models, query languages, scalability options, consistency models, community support, and advanced features, catering to different use cases and preferences in graph databases.

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Detailed Comparison

Dgraph	Amazon Neptune
Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC and HTTP.	Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency.
-	High performance and scalability; High availability and durability; Open Graph APIs; Highly secure; Fully managed; Fast parallel bulk data uploading; Cost-effective
Statistics
GitHub Stars 21.3K	GitHub Stars -
GitHub Forks 1.6K	GitHub Forks -
Stacks 124	Stacks 59
Followers 221	Followers 174
Votes 9	Votes 15
Pros & Cons
Pros 3 Graphql as a query language is nice if you like apollo 2 Easy set up 2 Low learning curve 1 High Performance 1 Open Source	Pros 3 Managed Service in AWS 3 High Performance 2 Support for RDF 2 Support for SPARQL 2 Easy to Use Cons 1 No UI to see graph
Integrations
No integrations available	Amazon S3 AWS IAM AWS Key Management Service Amazon CloudWatch

What are some alternatives to Dgraph, Amazon Neptune?

Neo4j

Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.

Graph Story

Graph Story offers fully-managed, fast, secure and affordable access to graph databases-as-a-service and makes them even easier to use through our customized API.

RedisGraph

RedisGraph is a graph database developed from scratch on top of Redis, using the new Redis Modules API to extend Redis with new commands and capabilities. Its main features include: - Simple, fast indexing and querying - Data stored in RAM, using memory-efficient custom data structures - On disk persistence - Tabular result sets - Simple and popular graph query language (Cypher) - Data Filtering, Aggregation and ordering

Cayley

Cayley is an open-source graph inspired by the graph database behind Freebase and Google's Knowledge Graph. Its goal is to be a part of the developer's toolbox where Linked Data and graph-shaped data (semantic webs, social networks, etc) in general are concerned.

Blazegraph

It is a fully open-source high-performance graph database supporting the RDF data model and RDR. It operates as an embedded database or over a client/server REST API.

TigerGraph DB

It is the only scalable graph database for the enterprise which is based on the industry’s first Native and Parallel Graph technology. It unleashes the power of interconnected data, offering organizations deeper insights and better outcomes. It’s proven technology supports applications such as IoT, AI and machine learning to make sense of ever-changing big data.

Graph Engine

The distributed RAM store provides a globally addressable high-performance key-value store over a cluster of machines. Through the RAM store, GE enables the fast random data access power over a large distributed data set.

FalkorDB

FalkorDB is developing a novel graph database that revolutionizes the graph databases and AI industries. Our graph database is based on novel but proven linear algebra algorithms on sparse matrices that deliver unprecedented performance up to two orders of magnitude greater than the leading graph databases. Our goal is to provide the missing piece in AI in general and LLM in particular, reducing hallucinations and enhancing accuracy and reliability. We accomplish this by providing a fast and interactive knowledge graph, which provides a superior solution to the common solutions today.

JanusGraph

It is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. It is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time.

Titan

Titan is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time.

Related Comparisons

Amazon Neptune vs Dgraph: What are the differences?

Data Model: Amazon Neptune uses a property graph model, while Dgraph utilizes a graph database model with RDF (Resource Description Framework) triples, providing different ways to visualize and interact with data.
Query Language: Amazon Neptune supports Apache TinkerPop Gremlin and SPARQL for querying data, whereas Dgraph uses GraphQL±, a query language specifically designed for graph databases, offering a more user-friendly approach.
Scalability: Amazon Neptune is a fully managed service in AWS, providing automated scaling capabilities, while Dgraph is open-source and requires manual scaling to handle increased workloads, offering different scalability options.
Consistency Model: Amazon Neptune offers strong consistency, ensuring data integrity, while Dgraph provides eventual consistency by default, allowing for more flexibility in data replication and synchronization.
Community Support: Dgraph has a growing community with active contributors, forums, and documentation, fostering open collaboration and development, whereas Amazon Neptune has a more established support system being a service within AWS, offering enterprise-grade support options.
Advanced Features: Amazon Neptune includes features like multi-region replication, integrated security with AWS IAM, and automated backups, providing advanced functionalities for enterprise use cases, while Dgraph focuses on features like distributed transactions, sharding for horizontal scaling, and ACID compliance.

Amazon Neptune vs Dgraph

Overview