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Akutan

6
31
+ 1
0
Dgraph

116
189
+ 1
9
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Beam vs Dgraph: What are the differences?

Developers describe Beam as "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. On the other hand, Dgraph is detailed as "Fast, Distributed Graph DB". 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.

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

Beam and Dgraph are both open source tools. It seems that Dgraph with 9.96K GitHub stars and 696 forks on GitHub has more adoption than Beam with 1.37K GitHub stars and 64 GitHub forks.

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Pros of Akutan
Pros of Dgraph
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    • 3
      Graphql as a query language is nice if you like apollo
    • 2
      Easy set up
    • 2
      Low learning curve
    • 1
      Open Source
    • 1
      High Performance

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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 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.

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    What companies use Akutan?
    What companies use Dgraph?
      No companies found
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      What tools integrate with Akutan?
      What tools integrate with Dgraph?
        No integrations found

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        What are some alternatives to Akutan and Dgraph?
        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