Fine-Grained Graph Versioning data storage: event sourcing
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Data Persistence configurable: on-machine, network, cloud, or custom
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Design Assumptions
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Graph Queries custom compiler, AST instructions to graph interpreter
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Historical Queries query the past
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Standing Queries
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Stateful Data Streams fast and durable
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Enterprise Architecture fits in between two Kalka streams
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Data-Driven Events
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Interpret High-Volume Data merge many sources, trigger action in real-time
Description:
Explore the innovative Quine streaming graph for modern data pipelines in this 47-minute webinar from Open Data Science. Dive into the inner workings of Quine, its challenges, and real-world applications. Learn about its property-graph data model, asynchronous graph computational model, and powerful capabilities. Discover how Quine implements Pregel with Actors, allowing nodes to perform arbitrary computations. Understand its integration with user-contributed "recipes" and explore its potential in enhancing data pipelines. Gain insights into graph data models, compute models, fine-grained versioning, data persistence options, and custom query capabilities. Examine historical queries, standing queries, stateful data streams, and enterprise architecture applications. Uncover Quine's ability to interpret high-volume data, merge multiple sources, and trigger real-time actions.
Introducing Quine - A Streaming Graph for Modern Data Pipelines