Akutan

4
28
+ 1
0
Google Cloud Dataflow

149
243
+ 1
3
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Beam vs Google Cloud Dataflow: What are the differences?

What is 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.

What is Google Cloud Dataflow? A fully-managed cloud service and programming model for batch and streaming big data processing. Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

Beam can be classified as a tool in the "Graph Databases" category, while Google Cloud Dataflow is grouped under "Real-time Data Processing".

Beam is an open source tool with 1.39K GitHub stars and 67 GitHub forks. Here's a link to Beam'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 Google Cloud Dataflow?

    Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
    What companies use Akutan?
    What companies use Google Cloud Dataflow?
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      What tools integrate with Akutan?
      What tools integrate with Google Cloud Dataflow?

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      What are some alternatives to Akutan and Google Cloud Dataflow?
      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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