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  1. Stackups
  2. Application & Data
  3. Graph Databases
  4. Graph Databases
  5. Blazegraph vs Grakn

Blazegraph vs Grakn

OverviewComparisonAlternatives

Overview

Blazegraph
Blazegraph
Stacks7
Followers16
Votes3
TypeDB
TypeDB
Stacks11
Followers34
Votes0

Blazegraph vs Grakn: What are the differences?

Blazegraph: An ultra-high performance database for big graphs offering both Semantic Web and Graph Database. 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; Grakn: An intelligent database - a knowledge graph. It is an intelligent database: a knowledge graph engine to organise complex networks of data and making it queryable, by performing knowledge engineering. Rooted in Knowledge Representation and Automated Reasoning, it provides the knowledge foundation for cognitive and intelligent (e.g. AI) systems, by providing an intelligent language for modelling, transactions and analytics. Being a distributed database, it is designed to scale over a network of computers through partitioning and replication.

Blazegraph and Grakn can be primarily classified as "Graph Databases" tools.

Some of the features offered by Blazegraph are:

  • High Performance Native graph database
  • Blueprints API or RDF/SPARQL
  • Single machine data storage to ~50B triples/quads (RWStore)

On the other hand, Grakn provides the following key features:

  • Distributed Analytics
  • Automated Reasoning
  • Higher-Level Language

Grakn is an open source tool with 2K GitHub stars and 234 GitHub forks. Here's a link to Grakn's open source repository on GitHub.

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Detailed Comparison

Blazegraph
Blazegraph
TypeDB
TypeDB

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.

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

High Performance Native graph database; Blueprints API or RDF/SPARQL; Single machine data storage to ~50B triples/quads (RWStore); REST API with embedded and/or webapp deployment (NanoSparqlServer); Fast 100% native SPARQL 1.1 evaluation; Fast RDFS+ inference and truth maintenance; Triples, quads, or Reification Done Right (RDR) support; 100% Java memory manager leverages the JVM native heap (no GC); Vertex-centric API
Distributed Analytics; Automated Reasoning; Higher-Level Language
Statistics
Stacks
7
Stacks
11
Followers
16
Followers
34
Votes
3
Votes
0
Pros & Cons
Pros
  • 1
    Support for SPARQL
  • 1
    Easy Setup and Use
  • 1
    Support for RDF
No community feedback yet
Integrations
Structr
Structr
Graph Story
Graph Story
Cartography
Cartography
GrapheneDB
GrapheneDB
Linkurious
Linkurious
No integrations available

What are some alternatives to Blazegraph, TypeDB?

Neo4j

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.

Dgraph

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

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

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.

Graph Engine

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

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

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

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.

Memgraph

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.

Nebula Graph

Nebula Graph

It is an open source distributed graph database. It has a shared-nothing architecture and scales quite well due to the separation of storage and computation. It can handle hundreds of billions of vertices and trillions of edges while still maintaining milliseconds of latency. It is openCypher compatible.

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