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

Beam vs Grakn

OverviewComparisonAlternatives

Overview

Akutan
Akutan
Stacks6
Followers32
Votes0
GitHub Stars1.7K
Forks105
TypeDB
TypeDB
Stacks11
Followers34
Votes0

Beam vs Grakn: What are the differences?

## Key Differences between Beam and Grakn

Beam and Grakn are two distinct technologies with specific use cases and functionalities. Here are the key differences between the two:

1. **Data Processing vs. Knowledge Graph**: Beam is primarily a distributed data processing framework, focusing on batch and stream processing, while Grakn is a knowledge graph database specializing in managing complex data relationships.
2. **Apache vs. Grakn Core**: Beam is developed under the Apache Software Foundation as an open-source project, whereas Grakn is a proprietary technology developed by Grakn Labs.
3. **Developers vs. Domain Experts**: Beam is designed for developers and data engineers to build data pipelines and processing applications, whereas Grakn caters to domain experts, allowing them to model and query complex knowledge graphs.
4. **Batch Processing vs. Transaction Support**: Beam excels in batch processing and stream processing scenarios, while Grakn offers transaction support for managing complex relationships and data integrity.
5. **Efficiency vs. Complexity**: Beam prioritizes efficiency and scalability in processing large volumes of data, while Grakn focuses on managing intricate data structures and semantic querying capabilities.
6. **Massive Scale vs. Knowledge Representation**: Beam is well-suited for handling large-scale data processing tasks across distributed systems, whereas Grakn shines in representing and querying complex knowledge structures effectively.

In Summary, Beam and Grakn differ in their primary focus on data processing and knowledge graph management, developer vs. domain expert orientation, underlying technology frameworks, transaction support, efficiency, and scalability, as well as the scale and representation of data handled.

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Detailed Comparison

Akutan
Akutan
TypeDB
TypeDB

A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.

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

-
Distributed Analytics; Automated Reasoning; Higher-Level Language
Statistics
GitHub Stars
1.7K
GitHub Stars
-
GitHub Forks
105
GitHub Forks
-
Stacks
6
Stacks
11
Followers
32
Followers
34
Votes
0
Votes
0
Integrations
Kubernetes
Kubernetes
Golang
Golang
Make
Make
Visual Studio Code
Visual Studio Code
Docker
Docker
Kafka
Kafka
RocksDB
RocksDB
gRPC
gRPC
OpenTracing
OpenTracing
Homebrew
Homebrew
No integrations available

What are some alternatives to Akutan, 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.

Blazegraph

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

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.

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