Search Results for "easy"

Benthos is a high performance and resilient stream processor, able to connect various sources and sinks in a range of brokering patterns and perform hydration, enrichments, transformations and filters on payloads. It comes with a powerful mapping language, is easy to deploy and monitor, and ready to drop into your pipeline either as a static binary, docker image, or serverless function, making it cloud native as heck. Delivery guarantees can be a dodgy subject. Benthos processes and acknowledges messages using an in-process transaction model with no need for any disk persisted state, so when connecting to at-least-once sources and sinks it's able to guarantee at-least-once delivery even in the event of crashes, disk corruption, or other unexpected server faults. ...

Go library for building event-driven applications. Our goal was to create a tool that is easy to understand, even by junior developers. It doesn't matter if you want to do Event-driven architecture, CQRS, Event Sourcing or just stream MySQL Binlog to Kafka. Watermill was designed to process hundreds of thousands of messages per second. Every component is built in a way that allows you to configure it for your needs. You can also implement your own middleware for the router.

...It offers an end-to-end system for ingesting raw data, applying transformations, filtering, and formatting outputs—all while remaining scalable and traceable. Fondant is designed with reproducibility in mind and supports containerized steps using Docker, making it easy to share and reuse data processing components. It’s built for use in research and production, empowering data scientists to streamline dataset curation and preprocessing workflows efficiently.

collapse is a high-performance R package designed for fast and efficient data transformation, aggregation, reshaping, and statistical computation. Built to offer a more performant alternative to dplyr and data.table, it is particularly well-suited for large datasets and econometric applications. It operates on base R data structures like data frames and vectors and uses highly optimized C++ code under the hood to deliver significant speed improvements. collapse also includes tools for...

Pyper is a Python-native orchestration and scheduling framework designed for modern data workflows, machine learning pipelines, and any task that benefits from a lightweight DAG-based execution engine. Unlike heavier platforms like Airflow, Pyper aims to remain lean, modular, and developer-friendly, embracing Pythonic conventions and minimizing boilerplate. It focuses on local development ergonomics and seamless transition to production environments, making it ideal for small teams and...

HStreamDB is an open-source, cloud-native streaming database for IoT and beyond. Modernize your data stack for real-time applications. By subscribing to streams in HStreamDB, any update of the data stream will be pushed to your apps in real-time, and this promotes your apps to be more responsive. You can also replace message brokers with HStreamDB and everything you do with message brokers can be done better with HStreamDB. HStreamDB provides built-in support for event time-based stream...

...The library is lightweight, well-documented, and ideal for research, prototyping, or teaching purposes. Padasip supports both supervised and unsupervised filtering modes and is built to be modular and extensible, making it easy to integrate into larger machine learning pipelines or control systems.

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 (...

Wally is a fast-stream-processing framework. Wally makes it easy to react to data in real-time. By eliminating infrastructure complexity, going from prototype to production has never been simpler. When we set out to build Wally, we had several high-level goals in mind. Create a dependable and resilient distributed computing framework. Take care of the complexities of distributed computing "plumbing," allowing developers to focus on their business logic.

Sed.py is a python module to provide a easy way to do text stream processing. Just like the name of module, it likes to do the work that sed can do. But not in sed's way, it's in Python's way. To use this module, the knowledge of regexp is necessary.