Search Results for "processing"
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Showing 250 open source projects for "processing"
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Apache Beam
Unified programming model for Batch and Streaming
Apache Beam is an open source, unified programming model to define both batch and streaming data-parallel processing pipelines, as well as certain language-specific SDKs for constructing pipelines and Runners. These pipelines are executed on one of Beam’s supported distributed processing back-ends, which include Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Beam is especially useful for Embarrassingly Parallel data processing tasks, and caters to the different needs and backgrounds of end users, SDK writers and runner writers. -
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Kapacitor
Open source framework for processing, monitoring, and alerting
Open source framework for processing, monitoring, and alerting on time series data. Kapacitor is a real-time data processing engine for monitoring and alerting, specifically designed to work with time-series data from InfluxDB. -
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Apache Spark
A unified analytics engine for large-scale data processing
Apache Spark is a unified engine for large-scale data processing, offering APIs for batch jobs, streaming, machine learning, and graph computation. It builds on resilient distributed datasets (RDDs) and the newer DataFrame/Dataset abstractions to provide fault-tolerant, in-memory computation across clusters. Spark’s execution engine handles scheduling, shuffles, caching, and data locality so users can focus on transformations rather than infrastructure plumbing. -
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Apache Flink
Stream processing framework with powerful stream
Apache Flink is a distributed engine for stateful computations over data streams and batches, designed for low-latency processing at scale. Its core runtime executes dataflow graphs with fine-grained backpressure and checkpointing, allowing applications to recover consistently from failures. Flink’s event-time model and watermarks enable accurate out-of-order processing, windowing, and complex time semantics that typical real-time systems struggle with. Developers program against high-level APIs—DataStream and Table/SQL—to express transformations, joins, and stateful patterns, while specialized libraries support CEP, machine learning workflows, and connectors. ... -
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Pachyderm
Data-Centric Pipelines and Data Versioning
...Automatically produces an immutable record for all activities and assets. Pachyderm is used across a variety of industries and use cases. Pachyderm provides a powerful solution to optimize data processing, MLOps, and ML Lifecycles. -
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Broadway
Concurrent and multi-stage data ingestion and data processing
...Developers can enrich pipelines with batching, concurrency control, and metrics reporting, simplifying the management of complex data ingestion and processing systems. Broadway is often used for event processing, stream handling, and background jobs, offering both performance and clarity in Elixir’s functional style. -
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SageMaker Spark Container
Docker image used to run data processing workloads
Apache Spark™ is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. -
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Spring Batch
Spring Batch is a framework for writing batch applications using Java
A lightweight, comprehensive batch framework designed to enable the development of robust batch applications vital for the daily operations of enterprise systems. Spring Batch provides reusable functions that are essential in processing large volumes of records, including logging/tracing, transaction management, job processing statistics, job restart, skip, and resource management. It also provides more advanced technical services and features that will enable extremely high-volume and high performance batch jobs through optimization and partitioning techniques. ... -
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Apache Sedona
Cluster computing framework for processing large-scale geospatial data
...According to our benchmark and third-party research papers, Sedona has 50% less peak memory consumption than other Spark-based geospatial data systems for large-scale in-memory query processing. Sedona offers Scala, Java, Spatial SQL, Python, and R APIs and integrates them into underlying system kernels with care. You can simply create spatial analytics and data mining applications and run them in any cloud environments. -
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Celery
Distributed task queue (development branch)
Celery is a simple, flexible, and reliable distributed system to process vast amounts of messages, while providing operations with the tools required to maintain such a system. It’s a task queue with focus on real-time processing, while also supporting task scheduling. Celery has a large and diverse community of users and contributors, you should come join us on IRC or our mailing-list. Celery is Open Source and licensed under the BSD License. A task queue’s input is a unit of work called a task. Dedicated worker processes constantly monitor task queues for new work to perform. ... -
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Benthos
Fancy stream processing made operationally mundane
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... -
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Membrane Core
The core of Membrane Framework, multimedia processing framework
membrane_core is the foundation of the Membrane multimedia framework for Elixir, providing the abstractions and runtime needed to build real-time audio and video pipelines. It models media processing as a graph of lightweight, supervised OTP processes—elements connected by links—so work is isolated, fault-tolerant, and easy to scale or reconfigure at runtime. The core defines a clear lifecycle and callback API for elements, plus concepts like buffers, events, and capabilities/format negotiation to keep components interoperable and type-safe. ... -
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NNCF
Neural Network Compression Framework for enhanced OpenVINO
NNCF (Neural Network Compression Framework) is an optimization toolkit for deep learning models, designed to apply quantization, pruning, and other techniques to improve inference efficiency. -
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Watermill
Building event-driven applications the easy way in Go
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. Watermill is... -
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BotSharp
AI Multi-Agent Framework in .NET
Conversation as a platform (CaaP) is the future, so it's perfect that we're already offering the whole toolkits to our .NET developers using the BotSharp AI BOT Platform Builder to build a CaaP. It opens up as much learning power as possible for your own robots and precisely control every step of the AI processing pipeline. BotSharp is an open source machine learning framework for AI Bot platform builder. This project involves natural language understanding, computer vision and audio processing technologies, and aims to promote the development and application of intelligent robot assistants in information systems. Out-of-the-box machine learning algorithms allow ordinary programmers to develop artificial intelligence applications faster and easier. ... -
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Hexagon
Hexagon is a microservices toolkit written in Kotlin
Hexagon is a microservices toolkit written in Kotlin. Its purpose is to ease the building of server applications (Web applications or APIs) that run inside a cloud platform. The Hexagon Toolkit provides several libraries to build server applications. These libraries provide single standalone features and are referred to as "Ports". -
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Botonic
Build chatbots and conversational experiences using React
Botonic is a full-stack Javascript framework to create chatbots and modern conversational apps that work on multiple platforms, web, mobile and messaging apps (Messenger, Whatsapp, Telegram, etc). Building modern applications on top of messaging apps like Whatsapp or Messenger is much more than creating simple text-based chatbots. Botonic is a full-stack serverless framework that combines the power of React and Tensorflow.js to create amazing experiences at the intersection of text and... -
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go-web-framework-benchmark
Go web framework benchmark
go-web-framework-benchmark is a benchmark suite for comparing the performance of Go web frameworks. It was created to measure full HTTP request processing instead of only route matching speed. The project runs small real HTTP servers for many stable frameworks and tests a simple /hello endpoint. It can add configurable handler delay to simulate business work such as database calls, cache access, disk writes, socket activity, or microservice calls. The benchmark uses wrk and scripts to collect throughput, latency, allocation, concurrency, pipelining, and CPU-bound results. ... -
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Posturr
A macOS app that blurs your screen when you slouch
...This approach turns posture correction into an interactive feedback loop that doesn’t require wearables or external sensors, prioritizing privacy because all image processing happens on the device with no cloud transmission. It’s helpful for people who spend long hours working at a desk and want a gentle reminder to maintain ergonomic alignment without intrusive notifications. -
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Argo Workflows
Workflow engine for Kubernetes
...Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a directed acyclic graph (DAG). Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using Argo Workflows on Kubernetes. Run CI/CD pipelines natively on Kubernetes without configuring complex software development products. Argo Workflows is the most popular workflow execution engine for Kubernetes. It can run 1000s of workflows a day, each with 1000s of concurrent tasks. Our users say it is lighter-weight, faster, more powerful, and easier to use. ... -
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gse
Go efficient multilingual NLP and text segmentation
Go efficient multilingual NLP and text segmentation; support English, Chinese, Japanese and others. Gse is implements jieba by golang, and try add NLP support and more feature. Support common, search engine, full mode, precise mode and HMM mode multiple word segmentation modes. Support user and embed dictionary, Part-of-speech/POS tagging, analyze segment info, stop and trim words. Support multilingual: English, Chinese, Japanese and others. Support Traditional Chinese. Support HMM cut text... -
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Asynq
Simple, reliable, and efficient distributed task queue in Go
Asynq is a Go library for queueing tasks and processing them asynchronously with workers. It's backed by Redis and is designed to be scalable yet easy to get started. Client puts tasks on a queue. Server pulls tasks off queues and starts a worker goroutine for each task. Tasks are processed concurrently by multiple workers. Task queues are used as a mechanism to distribute work across multiple machines. -
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BentoML
Unified Model Serving Framework
BentoML simplifies ML model deployment and serves your models at a production scale. Support multiple ML frameworks natively: Tensorflow, PyTorch, XGBoost, Scikit-Learn and many more! Define custom serving pipeline with pre-processing, post-processing and ensemble models. Standard .bento format for packaging code, models and dependencies for easy versioning and deployment. Integrate with any training pipeline or ML experimentation platform. Parallelize compute-intense model inference workloads to scale separately from the serving logic. Adaptive batching dynamically groups inference requests for optimal performance. ... -
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Smallpond
A lightweight data processing framework built on DuckDB and 3FS
smallpond is a lightweight distributed data processing framework built by DeepSeek, designed to scale DuckDB workloads over clusters using their 3FS (Fire-Flyer File System) backend. The idea is to preserve DuckDB’s fast analytics engine but lift it from single-node to multi-node settings, giving you the ability to operate on large datasets (e.g. petabyte scale) without moving to a heavyweight system like Spark. -
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Nuclio
High-Performance Serverless event and data processing platform
Nuclio is an open source and managed serverless platform used to minimize development and maintenance overhead and automate the deployment of data-science-based applications. Real-time performance running up to 400,000 function invocations per second. Portable across low laptops, edge, on-prem and multi-cloud deployments. The first serverless platform supporting GPUs for optimized utilization and sharing. Automated deployment to production in a few clicks from Jupyter notebook. Deploy one of...
