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
Overview
Syllabus
Intro
Name Collisions the trine, the quine, the trine
The Streaming Problem
Introducing Quine
What is it good for? some Quine use cases
Graph Data Model
Graph Compute Model the Actor Model
Fine-Grained Graph Versioning data storage: event sourcing
Data Persistence configurable: on-machine, network, cloud, or custom
Design Assumptions
Graph Queries custom compiler, AST instructions to graph interpreter
Historical Queries query the past
Standing Queries
Stateful Data Streams fast and durable
Enterprise Architecture fits in between two Kalka streams
Data-Driven Events
Interpret High-Volume Data merge many sources, trigger action in real-time
Taught by
Open Data Science
