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Akutan

5
30
+ 1
0
Arc

42
34
+ 1
0
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Akutan vs Arc: What are the differences?

Akutan: 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; Arc: A dialect of the Lisp programming language developed by Paul Graham and Robert Morris. 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.

Akutan belongs to "Graph Databases" category of the tech stack, while Arc can be primarily classified under "Languages".

Akutan is an open source tool with 1.57K GitHub stars and 89 GitHub forks. Here's a link to Akutan's open source repository on GitHub.

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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 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.

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    What tools integrate with Akutan?
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    What are some alternatives to Akutan and Arc?
    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.
    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'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.
    See all alternatives