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Akutan vs DSE Graph

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Akutan
6
32
+ 1
0
DSE Graph
4
8
+ 1
0
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Beam vs DSE Graph: What are the differences?

Beam: A Distributed Knowledge Graph Store. 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; DSE Graph: A distributed graph database that is optimized for enterprise applications. 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.

Beam and DSE Graph can be primarily classified as "Graph Databases" tools.

Beam and DSE Graph are both open source tools. Beam with 1.43K GitHub stars and 67 forks on GitHub appears to be more popular than DSE Graph with 7 GitHub stars and 2 GitHub forks.

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What is Akutan?

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.

What is DSE Graph?

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.

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What companies use Akutan?
What companies use DSE Graph?
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    What tools integrate with Akutan?
    What tools integrate with DSE Graph?

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    What are some alternatives to Akutan and DSE Graph?
    Apache Beam
    It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    Apache Flink
    Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
    Arc
    Arc is designed for exploratory programming: the kind where you decide what to write by writing it. A good medium for exploratory programming is one that makes programs brief and malleable, so that's what we've aimed for. This is a medium for sketching software.
    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.
    See all alternatives

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