35 रिपॉजिटरी
Systems designed to distribute computational tasks across multiple threads or nodes to improve throughput and reduce latency.
Distinguishing note: Focuses on the architectural pattern of parallel task distribution rather than specific data storage or low-level system administration.
Explore 35 awesome GitHub repositories matching software engineering & architecture · Parallel Processing Pipelines. Refine with filters or upvote what's useful.
FFmpeg is a cross-platform multimedia framework designed for the recording, conversion, and streaming of audio and video content. It functions as a comprehensive toolkit that provides both a command-line utility for direct media manipulation and a collection of low-level libraries for integration into custom applications. At its core, the project utilizes a packet-based stream engine and a format-agnostic abstraction layer to handle diverse media standards, containers, and network protocols. The framework distinguishes itself through a modular, graph-based filter execution model that allows f
Executes transcoding pipeline components concurrently across multiple threads to improve throughput and CPU utilization.
Langextract is a framework designed to transform unstructured text into structured, machine-readable data using language model orchestration. It provides a high-performance pipeline that processes large volumes of narrative text by utilizing parallel execution and sequential extraction passes. The library is built to handle complex data extraction tasks, including specialized support for clinical information and medical entity relationship recognition. The project distinguishes itself through a plugin-based architecture that supports both local hardware execution and cloud-hosted model endpoi
Distributes extraction tasks across multiple concurrent threads to maximize throughput and reduce latency.
Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q
Distributes processing tasks across multiple branches simultaneously to improve throughput and analysis.
This project is a unified toolchain for JavaScript and TypeScript development, providing a suite of specialized tools for bundling, parsing, linting, and formatting source code. The toolchain is implemented in Rust and utilizes a shared internal representation to allow multiple tools to operate on the same parsed source code. It employs a parallelized analysis pipeline and single-pass parsing with error recovery to process files across CPU cores. The suite covers several core capability areas, including static analysis for identifying programmatic errors, automated style standardization, and
Employs a parallelized analysis pipeline to process multiple files simultaneously across CPU cores.
Presto is a distributed SQL query engine designed for high-performance analytical processing across heterogeneous data sources. It functions as a data federation platform and massively parallel processing engine, allowing users to execute interactive queries against diverse storage systems without requiring data migration. By mapping remote metadata and structures to a unified relational namespace, it enables seamless cross-platform analysis through a standard SQL interface. The engine distinguishes itself through a pluggable connector architecture and a shared-nothing distributed processing
Coordinates query execution across worker nodes to process large-scale datasets in parallel.
This project is a structured educational program and comprehensive training curriculum designed to teach the end-to-end lifecycle of machine learning models. It serves as a resource for engineers to master the transition of data science projects from development into reliable, production-ready systems. The curriculum focuses on the practical application of engineering best practices, emphasizing the orchestration of complex data processing and training sequences. It provides instruction on building repeatable workflows, managing experiment metadata, and implementing infrastructure automation
Orchestrates complex data processing and training sequences using dependency-aware directed acyclic graphs.
Compromise is a natural language processing library and rule-based text parser designed to analyze unstructured text. It functions as a toolkit for identifying parts of speech, linguistic patterns, and semantic meaning, while providing specialized engines for named entity recognition and the parsing of temporal and numeric data. The project is distinguished by its linguistic morphological engine, which can conjugate verbs across different tenses and inflect nouns and adjectives. It further allows for linguistic model customization through a plugin system that enables the extension of lexicons
Distributes the parsing of large text volumes across multiple CPU cores to increase throughput.
PyOD is a Python anomaly detection library used to identify outliers in tabular, time series, graph, text, and image data. It provides a collection of algorithms for detecting anomalous data points and includes a unified detector interface that standardizes input and output signatures across its available detection algorithms. The project features a multi-modal outlier detector for identifying anomalies across diverse formats including unstructured text and images, as well as a specialized toolkit for graph-based and time-series anomaly detection. It includes an ensemble framework for combini
Combines multiple detection models into a parallel execution pipeline to improve processing throughput.
Storm is a distributed stream processing framework and fault-tolerant compute engine designed for executing real-time continuous computations across a cluster of machines. It functions as a stateful stream processor and cluster topology manager, enabling the deployment and monitoring of distributed data flow configurations. The system ensures exactly-once semantics by utilizing transactional state management to guarantee that every message in a data stream is processed exactly one time. It further operates as a distributed RPC system, allowing for the integration of non-native languages throu
Defines data flow topologies as directed acyclic graphs to manage task dependencies and processing sequences.
Mage AI एक Python-आधारित डेटा पाइपलाइन ऑर्केस्ट्रेटर और सेल्फ-होस्टेड डेटा इंटीग्रेटेड डेवलपमेंट एनवायरनमेंट (IDE) है। इसे ब्लॉक-आधारित पाइपलाइन डिज़ाइन और इंटरैक्टिव नोटबुक इंटरफ़ेस का उपयोग करके डेटा वर्कफ़्लो बनाने, शेड्यूल करने और मॉनिटर करने के लिए डिज़ाइन किया गया है। यह प्लेटफ़ॉर्म जेनेरेटिव AI क्षमताओं को एकीकृत करके खुद को अलग बनाता है, जिससे उपयोगकर्ताओं को स्वचालित डेटा स्ट्रीम में आर्टिफिशियल इंटेलिजेंस को शामिल करने के लिए API के माध्यम से लार्ज लैंग्वेज मॉडल प्रोवाइडर्स से जुड़ने की अनुमति मिलती है। यह एक Apache Spark डेटा प्रोसेसर के रूप में भी कार्य करता है, जो उच्च-वॉल्यूम एनालिटिक्स और बड़े पैमाने पर डेटा प्रोसेसिंग के लिए आवश्यक कर्नल्स और इंफ्रास्ट्रक्चर को प्रबंधित करता है। यह सिस्टम ETL वर्कफ़्लो ऑटोमेशन, dbt मॉडल मैनेजमेंट, और डेटा स्ट्रीम डिस्कवरी सहित डेटा इंजीनियरिंग क्षमताओं की एक विस्तृत श्रृंखला को कवर करता है। यह Git के माध्यम से वर्ज़न कंट्रोल इंटीग्रेशन, कंटेनराइज़्ड डिप्लॉयमेंट, और डेवलपमेंट व प्रोडक्शन वातावरण में पाइपलाइन्स को प्रबंधित करने के लिए रोल-आधारित एक्सेस कंट्रोल के लिए टूल्स प्रदान करता है। मॉनिटरिंग को सिस्टम परफॉर्मेंस टेलीमेट्री और पाइपलाइन निष्पादन डिबगिंग के माध्यम से संभाला जाता है।
Implements a block-based design that sequences data loading and transformation steps into a directed acyclic graph.
Swarms एक मल्टी-एजेंट ऑर्केस्ट्रेशन फ्रेमवर्क और स्वायत्त एजेंट टूलकिट है जिसे लार्ज लैंग्वेज मॉडल एजेंटों को समन्वयित करने के लिए डिज़ाइन किया गया है। यह एजेंट संबंधों को प्रबंधित करने के लिए एक वर्कफ़्लो इंजन के रूप में कार्य करता है, जो एकीकृत मेमोरी, टूल-कॉलिंग क्षमताओं और रीजनिंग लूप के साथ स्वायत्त एजेंट बनाने के लिए इंफ्रास्ट्रक्चर प्रदान करता है। फ्रेमवर्क को इसकी मल्टी-एजेंट सर्वसम्मति प्रणालियों द्वारा प्रतिष्ठित किया जाता है, जो उच्च-गुणवत्ता वाली प्रतिक्रियाओं को संश्लेषित करने के लिए वोटिंग, प्रतिकूल बहस और जज एजेंटों का उपयोग करती हैं। यह सहयोग पैटर्न की एक विविधता का समर्थन करता है, जिसमें डायरेक्टर-वर्कर पदानुक्रम, विशेषज्ञ संश्लेषण और प्राकृतिक भाषा विवरणों के आधार पर स्वचालित स्वार्म आर्किटेक्चर निर्माण शामिल है। सिस्टम परिचालन क्षमताओं की एक विस्तृत श्रृंखला को कवर करता है, जिसमें डोमेन-विशिष्ट भाषा के माध्यम से ग्राफ़-आधारित और अनुक्रमिक वर्कफ़्लो ऑर्केस्ट्रेशन, विविध मॉडल प्रदाताओं के लिए एकीकृत इंटरफेसिंग, और डायनेमिक टूल डिस्कवरी के लिए Model Context Protocol के साथ एकीकरण शामिल है। इसमें रिट्रीवल ऑगमेंटेड जनरेशन, स्टेट-पर्सिस्टेंट मेमोरी और एजेंट कार्यक्षमता को वेब सेवाओं के रूप में उजागर करने की क्षमता के लिए समर्थन भी शामिल है। प्रोजेक्ट एजेंट प्रबंधन के लिए एक कमांड-लाइन इंटरफेस प्रदान करता है और YAML तथा मॉड्यूलर मार्कडाउन स्किल फ़ाइलों के माध्यम से कॉन्फ़िगरेशन का समर्थन करता है।
Organizes agent execution as a directed acyclic graph to manage complex dependencies and automatic parallelism.
ClearML is a comprehensive MLOps platform designed to manage the entire machine learning lifecycle. It functions as an experiment tracking tool, a data versioning system, and a pipeline orchestrator, while providing infrastructure for GPU cluster management and model serving. The platform is distinguished by its ability to handle hybrid-cloud compute scheduling and fractional GPU allocation, allowing multiple workloads to share a single hardware accelerator. It employs a metadata-based approach to data versioning, using virtual views to track large datasets and artifacts without duplicating r
Defines machine learning workflows as a directed acyclic graph of dependent tasks and execution queues.
Storm is a distributed stream processing framework designed to execute unbounded computations across a cluster to process real-time data streams. It functions as a data pipeline orchestrator that allows users to define and deploy declarative data flow graphs connecting streaming sources to processing components. The system operates as a multi-tenant distributed compute engine that isolates workloads and limits resource usage across shared clusters using dedicated pools and access control. It is also a secure distributed processing engine that employs encrypted node communication and SSL-secur
Implements data processing using directed acyclic graph pipelines to route tuples through interconnected spouts and bolts.
Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis
Transforms data processing logic into a directed acyclic graph to enable parallel, multi-stage execution across the cluster.
Rushstack बड़े पैमाने पर TypeScript मोनोरेपोज़ को प्रबंधित करने के लिए एक व्यापक टूलसेट है, जो बिल्ड पाइपलाइन ऑटोमेशन, डिपेंडेंसी समन्वय और स्टेटिक एनालिसिस के लिए एक फ्रेमवर्क प्रदान करता है। यह एक इंक्रीमेंटल बिल्ड ऑर्केस्ट्रेटर और प्रबंधन प्रणाली के रूप में कार्य करता है जिसे शेयर्ड वर्कस्पेस में कई पैकेजों में स्थिरता और प्रदर्शन बनाए रखने के लिए डिज़ाइन किया गया है। यह सिस्टम डायरेक्टेड एसाइक्लिक ग्राफ्स और कंटेंट-हैश-आधारित इंक्रीमेंटलिज़्म पर आधारित निष्पादन मॉडल के माध्यम से खुद को अलग करता है, जो यह सुनिश्चित करता है कि केवल प्रभावित प्रोजेक्ट्स ही फिर से बनाए जाएं। यह S3, Azure और HTTP के लिए एकीकरण के साथ रिमोट बिल्ड आर्टिफैक्ट कैशिंग के माध्यम से विकास वर्कफ़्लो को और अधिक ऑप्टिमाइज़ करता है, और स्थानीय परिवर्तनों की तत्काल दृश्यता के लिए सिमलिनक-आधारित वर्कस्पेस प्रबंधन का उपयोग करता है। टूलसेट स्वचालित रिलीज़ वर्कफ़्लो, API कॉन्ट्रैक्ट विश्लेषण, और मानकीकृत लिंटिंग प्रोफाइल के माध्यम से एंटरप्राइज़ कोड गुणवत्ता प्रवर्तन सहित क्षमताओं की एक विस्तृत श्रृंखला को कवर करता है। यह एसेट बंडलिंग, TypeScript ट्रांसपाइलेशन, और JSON स्कीमा और स्टेटिक एसेट्स से टाइप डेफिनिशन के निर्माण के लिए व्यापक समर्थन भी प्रदान करता है। अतिरिक्त उपयोगिता क्षेत्रों में स्थानीयकरण प्रबंधन, लाइसेंस नोटिस जनरेशन, और सिंक्रोनाइज़्ड प्रोसेस आउटपुट के साथ कमांड-लाइन इंटरफेस का ऑर्केस्ट्रेशन शामिल है।
Orchestrates interdependent build tasks using directed acyclic graphs to ensure correct processing order.
Osmedeus is a security workflow orchestration engine that coordinates AI agents, shell commands, and scanning tools through declarative YAML pipelines. It functions as a distributed security scanner, a declarative workflow automator, and an AI agent framework for security, enabling automated multi-step security analysis with conditional branching, parallel execution, and distributed workers. The engine distinguishes itself through a hybrid runner model that executes workflow steps on the local host, inside Docker containers, or over SSH to remote machines, selected per step or module. It supp
Resolves module dependencies into a DAG for parallel and ordered execution in security workflows.
Wireit is a build system orchestrator and npm script task runner that manages complex dependencies between scripts. It functions as a development workflow automator by tracking input files and dependencies to skip redundant executions and optimize total build time. The project features a content-addressable build cache that saves generated files to bypass expensive computations. It includes a file watcher that monitors project changes and automatically triggers only the affected scripts within a dependency graph. The tool covers task orchestration through parallel script execution, backgroun
Orchestrates script execution by building a directed acyclic graph to manage dependencies and maximize parallelism.
Pachyderm is a containerized, versioned, and lineage-tracked data pipeline platform that runs natively on Kubernetes. It combines a distributed file system backend with immutable data versioning, so every commit to a data repository creates an auditable snapshot, and every pipeline step executes as an isolated container. The platform is defined by a data-centric pipeline model where pipelines are specified by their input and output data repositories rather than explicit task sequences, and provenance is recorded as a directed acyclic graph of commits linking output data to its input sources an
Data lineage is recorded as a directed acyclic graph of commits, linking output data to its input sources and pipeline versions.
pgloader is a command-line tool that automates the migration of data and schema from various source databases and file formats into PostgreSQL. It combines schema discovery, parallel data pipelines, and type casting into a single, declarative workflow, using PostgreSQL's COPY protocol for high-throughput bulk loading. The tool distinguishes itself by compiling a dedicated command language into concurrent reader-writer pipelines that handle schema introspection, data transformation, and error-resilient batch processing. It supports migrating entire databases from MySQL, MS SQL, SQLite, and Pos
Runs concurrent reader-writer pipelines per table to maximize PostgreSQL bulk load throughput.
MSBuild is a .NET build engine and software build automation system that transforms source code into deployable applications. It utilizes a declarative project system based on a structured XML schema to define build items, properties, and targets used across the .NET ecosystem and Visual Studio. The system features a customizable build task framework that allows the implementation of custom tasks and loggers to perform specific software construction operations. This extensibility enables the creation of specialized logic to modify build process behavior. The engine manages build automation t
Uses directed acyclic graphs to manage the execution order of build targets based on their dependency requirements.