Stream Processing
Showing 36 of 268 projects
A Python toolkit for developing, testing, and managing Apache Storm streaming data processing topologies.
A Go library for packing and unpacking binary data streams with support for multiple data types and endianness.
A lightweight stream processing engine designed specifically for IoT data processing and analytics.
Merge multiple Node.js streams into one interleaved stream for parallel processing in gulp and other stream-based workflows.
A Python toolkit for text-focused data science on medium-sized datasets, bridging memory and cluster-scale processing.
A .NET standard for asynchronous stream processing with non-blocking backpressure, enabling interoperability between reactive libraries.
A blazingly fast, highly scalable graph-based stream processing framework for latency-critical applications like electronic trading and real-time AI.
A collection of reusable Delphi units providing Win32 wrappers, thread-safe structures, file utilities, and VCL enhancements.
A distributed video processing platform built on Apache Storm with OpenCV integration for large-scale computer vision pipelines.
A type-safe functional Stream processing library for Go, inspired by the Java Streams API.
A sample application demonstrating real-time data processing and visualization using Amazon Kinesis, DynamoDB, and a web server.
A Go library for building data processing workflows and pipelines with functional operations, cycles, and fan-out capabilities.
A visual development platform for building, deploying, and managing streaming analytics applications with multiple engine bindings.
Daggy - Data Aggregation Utility and C/C++ developer library for data streams catching
A Gulp plugin that executes shell commands on files in your pipeline.
A PHP library providing Python-inspired iteration tools for efficient data processing with loops and streams.
A Gulp plugin that remembers and recalls files passed through it, enabling incremental builds with full file sets.
A Ruby interface for the Amazon Kinesis Client Library, enabling developers to build robust streaming data applications.
A functional programming library for Go that introduces streams, collectors, and functional paradigms to improve code readability and reduce bugs.
Reactive programming library for F# Elmish and Fable applications using Async Observables.
A high-level Node.js module for creating readable streams with proper backpressure handling and a familiar API.
A Swift framework for launching shell tasks with ReactiveSwift-based stream processing.
A Go library for writing Storm spouts and bolts that communicate with Storm shells via the multilang protocol.
A MapReduce-style framework for processing fast/streaming data, implementing the MapUpdate model.
A CLI tool for extracting patterns from streaming unstructured log messages using the Drain parser algorithm.
A parser for the Test Anything Protocol (TAP) that converts TAP output into structured JavaScript objects.
A Java implementation of composable algorithmic transformations called transducers, independent from input/output sources.
A synchronous transform stream for Node.js with 10x performance over standard Transform streams.
A Storm spout that fetches data records from Amazon Kinesis and emits them as tuples for real-time stream processing.
A Node.js library for parsing and creating Web ARChive (WARC) files with support for Chrome, Puppeteer, and Electron.
A Java adapter that enables Amazon Kinesis Client Library (KCL) to consume and process data from DynamoDB Streams.
A Logstash input plugin that reads data from DynamoDB tables via table scans and DynamoDB Streams for near real-time data processing.
A parallelized stream implementation for Elixir that maintains order while processing with a worker pool.
Adds join operators to RxJava for combining multiple observable sequences based on pattern matching.
An experimental integration of Apache Samza and Luwak for scalable real-time document matching against large query sets.
A command-line tool that humanizes JSON streams by extracting and formatting specific fields for easier reading.
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