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Dataflow Programming for Big Engineering Data
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This paper is intended to review the history and findings of the American Consulting Engineers Council (ACEC) peer review program from its beginning in 1984 to ...
Dataflow programming
Wikipedia
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Dataflow programming is a programming paradigm that models a program as a directed graph of the data flowing between operations.
A survey of dataflow programming models and tools for big ...
infocomm-journal.com
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由 邹骁锋 著作 — The main methods of dataflow programming and the combination with the mainstream programming tools were analyzed,and the basic framework and ...
Dataflow for Data Processing in GCP : A Beginner's Guide
Medium
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2024年7月29日 — Google Cloud Dataflow is a unified stream and batch data processing service that allows you to create data pipelines to read from one or more sources.
Big Data Engineering — Declarative Data Flows
Dimajix
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This series is about building data pipelines with Apache Spark for batch processing. But some aspects are also valid for other frameworks or for stream ...
Dataflow documentation
Google Cloud
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With Dataflow, you can run your highly parallel workloads in a single pipeline, improving efficiency and making your workflow easier to manage.
Dataflow model and its applications in big data processing
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由 毕倪飞 著作被引用 1 次 — On other hand,the dataflow programming model of the dataflow model in big data processing was analyzed from the level of unified programming.
Implementation techniques for efficient data-flow analysis ...
IEEE Xplore
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IEEE Xplore
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由 DC Atkinson 著作2001被引用 64 次 — We present techniques that reduce the time and space required to perform dataflow analysis of large programs.
How To Write Large Programs
Medium
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Medium
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2019年1月11日 — I will show you two specific techniques suitable for large scale software development: modular programming and dataflow programming.
Data Engineering Best Practices - #1. Data flow & Code
Start Data Engineering
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Start Data Engineering
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2023年7月20日 — This post will review best practices for designing data pipelines, understanding industry standards, thinking about data quality, and writing maintainable and ...