DSE Graph vs Neo4j

Overview

Neo4j

Stacks1.2K

Followers1.4K

Votes351

GitHub Stars15.3K

Forks2.5K

DSE Graph

Stacks4

Followers8

Votes0

GitHub Stars0

Forks0

DSE Graph vs Neo4j: What are the differences?

1. DataStax Enterprise (DSE) Graph is an integrated graph database that is part of the DataStax Enterprise platform, providing enterprise-grade features and support. On the other hand, Neo4j is a standalone native graph database system known for its strong support for graph data models and query languages.

2. Scalability: DSE Graph is built on Apache Cassandra, a highly scalable distributed database, allowing it to easily scale out across multiple nodes. In contrast, Neo4j has limitations in terms of scalability due to its architecture being primarily focused on single-server deployments.

3. Tunable Consistency: DSE Graph offers tunable consistency levels, allowing users to adjust consistency based on the specific needs of their graph workloads. However, Neo4j has a strong focus on strong consistency by default, which may impact performance in certain scenarios.

4. Performance: Due to its distributed nature and integration with Apache Cassandra, DSE Graph excels in handling large-scale graph data and complex queries with high performance. Neo4j, while strong in query performance on smaller datasets, may face scalability challenges as the data grows.

5. Enterprise Features: DSE Graph comes with advanced enterprise features like robust security, integrated analytics, continuous availability, and global distribution capabilities. In comparison, Neo4j offers a more community-driven approach with a focus on developer experience and ease of use.

6. Ecosystem and Integration: DSE Graph seamlessly integrates with other components of the DataStax Enterprise platform, such as Apache Spark for analytics and Apache Kafka for real-time data processing. Neo4j, on the other hand, has a rich ecosystem of tools and libraries but may require additional integration efforts for full ecosystem support.

In Summary, DSE Graph offers scalability, tunable consistency, high performance, enterprise features, and seamless integration with the DataStax ecosystem, while Neo4j excels in graph data modeling and query languages but may have limitations in scalability and enterprise support.

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CLI (Node.js)

Manual

Detailed Comparison

Neo4j	DSE Graph
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.	It is a distributed graph database that is optimized for enterprise applications–Zero downtime, fast traversals at scale, and analysis of complex, related datasets in real time.
intuitive, using a graph model for data representation;reliable, with full ACID transactions;durable and fast, using a custom disk-based, native storage engine;massively scalable, up to several billion nodes/relationships/properties;highly-available, when distributed across multiple machines;expressive, with a powerful, human readable graph query language;fast, with a powerful traversal framework for high-speed graph queries;embeddable, with a few small jars;simple, accessible by a convenient REST interface or an object-oriented Java API	Graph Powered Insights; Graph Your Way; Graph Available Always
Statistics
GitHub Stars 15.3K	GitHub Stars 0
GitHub Forks 2.5K	GitHub Forks 0
Stacks 1.2K	Stacks 4
Followers 1.4K	Followers 8
Votes 351	Votes 0
Pros & Cons
Pros 69 Cypher – graph query language 61 Great graphdb 33 Open source 31 Rest api 27 High-Performance Native API Cons 9 Comparably slow 4 Can't store a vertex as JSON 1 Doesn't have a managed cloud service at low cost	No community feedback yet
Integrations
No integrations available	Python .NET JavaScript Java Groovy

What are some alternatives to Neo4j, DSE Graph?

Dgraph

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.

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.

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.

TypeDB

TypeDB is a database with a rich and logical type system. TypeDB empowers you to solve complex problems, using TypeQL as its query language.

Memgraph

Memgraph is a streaming graph application platform that helps you wrangle your streaming data, build sophisticated models that you can query in real-time, and develop applications you never thought possible in days, not months.