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Beam
Beam

4
15
+ 1
0
Google Cloud Dataflow
Google Cloud Dataflow

93
94
+ 1
0
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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.

- No public GitHub repository available -

What is Beam?

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.
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            What are some alternatives to Beam 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.
            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.
            Titan
            Titan is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time.
            JanusGraph
            It is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. It is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time.
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