Akutan vs Dgraph vs RedisGraph

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Akutan

6
32
+ 1
0
Dgraph

126
219
+ 1
9
RedisGraph

31
107
+ 1
7
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Pros of Akutan
Pros of Dgraph
Pros of RedisGraph
    Be the first to leave a pro
    • 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
    • 3
      10x – 600x faster than any other graph database
    • 2
      Cypher – graph query language
    • 1
      Great graphdb
    • 1
      Open source

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    - No public GitHub repository available -

    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.

    What is 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

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    What companies use Akutan?
    What companies use Dgraph?
    What companies use RedisGraph?
      No companies found

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      What tools integrate with Akutan?
      What tools integrate with Dgraph?
      What tools integrate with RedisGraph?
        No integrations found

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        What are some alternatives to Akutan, Dgraph, and RedisGraph?
        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