Search Results for "parallel"

Numaflow is a Kubernetes-native tool for running massively parallel stream processing. A Numaflow Pipeline is implemented as a Kubernetes custom resource and consists of one or more source, data processing, and sink vertices. Numaflow installs in a few minutes and is easier and cheaper to use for simple data processing applications than a full-featured stream processing platform.

...It abstracts cloud providers like IBM Cloud, AWS, Azure, and Google Cloud into a unified interface and turns your Python functions into scalable, event-driven workloads. Lithops is ideal for data processing, ML inference, and embarrassingly parallel workloads, giving you the power of FaaS (Function-as-a-Service) without vendor lock-in. It also supports hybrid cloud setups, object storage access, and simple integration with Jupyter notebooks.

...In computing, a pipeline, also known as a data pipeline, is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion. Some amount of buffer storage is often inserted between elements.

...The Actor Model provides a higher level of abstraction for writing concurrent and distributed systems. It alleviates the developer from having to deal with explicit locking and thread management, making it easier to write correct concurrent and parallel systems. Grain abstraction, which provides a straightforward approach to building distributed interactive applications, without the need to learn complex programming patterns for handling concurrency, fault tolerance, and resource management. This allows Proto.Actor to leverage in-process performance for realtime stream processing.

...This is especially useful for NLP tasks where duplicated training data can skew model performance. text-dedup scales to billions of documents and offers tools for chunking, hashing, and comparing text efficiently with low memory usage. It supports Jaccard similarity thresholding, parallel execution, and flexible deduplication strategies, making it ideal for cleaning web-scraped data, language model training datasets, or document archives.

Amadeus is a high-performance, distributed data processing framework written in Rust, designed to offer an ergonomic and safe alternative to tools like Apache Spark. It provides both streaming and batch capabilities, allowing users to work with real-time and historical data at scale. Thanks to Rust’s memory safety and zero-cost abstractions, Amadeus delivers performance gains while reducing the complexity and bugs common in large-scale data pipelines. It emphasizes developer productivity...

Webite: http://flowbasedprogramming.com DSPatch, pronounced "dispatch", is a powerful C++ dataflow framework. DSPatch is not limited to any particular domain or data type, from reactive programming to stream processing, DSPatch's generic, object-oriented API allows you to create virtually any dataflow system imaginable. *See also:* DSPatcher ( https://github.com/MarcusTomlinson/DSPatcher ): A cross-platform graphical tool for building DSPatch circuits. DSPatchables (...