Open Source Linux Stream Processing Tools
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Stream Processing Tools for Linux
View 7 business solutionsBrowse free open source Stream Processing tools and projects for Linux below. Use the toggles on the left to filter open source Stream Processing tools by OS, license, language, programming language, and project status.
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Best-of Python
A ranked list of awesome Python open-source libraries
This curated list contains 390 awesome open-source projects with a total of 1.4M stars grouped into 28 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! Ranked list of awesome python libraries for web development. Correctly generate plurals, ordinals, indefinite articles; convert numbers. Libraries for loading, collecting, and extracting data from a variety of data sources and formats. Libraries for data batch- and stream-processing, workflow automation, job scheduling, and other data pipeline tasks. -
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Acl
A powerful server and network library, including coroutine
The Acl (Advanced C/C++ Library) project a is powerful multi-platform network communication library and service framework, supporting LINUX, WIN32, Solaris, FreeBSD, MacOS, AndroidOS, iOS. Many applications written by Acl run on these devices with Linux, Windows, iPhone and Android and serve billions of users. There are some important modules in Acl project, including network communcation, server framework, application protocols, multiple coders, etc. The common protocols such as HTTP/SMTP/ICMP//MQTT/Redis/Memcached/Beanstalk/Handler Socket are implemented in Acl, and the codec library such as XML/JSON/MIME/BASE64/UUCODE/QPCODE/RFC2047/RFC1035, etc., are also included in Acl. Acl also provides a unified abstract interface for popular databases such as Mysql, Postgresql, Sqlite. Using Acl library users can write database applications more easily, quickly and safely. -
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fluentbit
Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX
Fluent Bit is a super-fast, lightweight, and highly scalable logging and metrics processor and forwarder. It is the preferred choice for cloud and containerized environments. A robust, lightweight, and portable architecture for high throughput with low CPU and memory usage from any data source to any destination. Proven across distributed cloud and container environments. Highly available with I/O handlers to store data for disaster recovery. Granular management of data parsing and routing. Filtering and enrichment to optimize security and minimize cost. The lightweight, asynchronous design optimizes resource usage: CPU, memory, disk I/O, network. No more OOM errors! Integration with all your technology, cloud-native services, containers, streaming processors, and data backends. Fully event-driven design leverages the operating system API for performance and reliability. All operations to collect and deliver data are asynchronous. -
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Pathway
Python ETL framework for stream processing, real-time analytics, LLM
Pathway is an open-source framework designed for building real-time data applications using reactive and declarative paradigms. It enables seamless integration of live data streams and structured data into analytical pipelines with minimal latency. Pathway is especially well-suited for scenarios like financial analytics, IoT, fraud detection, and logistics, where high-velocity and continuously changing data is the norm. Unlike traditional batch processing frameworks, Pathway continuously updates the results of your data logic as new events arrive, functioning more like a database that reacts in real-time. It supports Python, integrates with modern data tools, and offers a deterministic dataflow model to ensure reproducibility and correctness. -
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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 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. This behaviour is the default and free of caveats, which also makes deploying and scaling Benthos much simpler. -
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Akka
Build concurrent, distributed, and resilient message-driven apps
Build powerful reactive, concurrent, and distributed applications more easily. Akka is a toolkit for building highly concurrent, distributed, and resilient message-driven applications for Java and Scala. Actors and Streams let you build systems that scale up, using the resources of a server more efficiently, and out, using multiple servers. Building on the principles of The Reactive Manifesto Akka allows you to write systems that self-heal and stay responsive in the face of failures. Up to 50 million msg/sec on a single machine. Small memory footprint; ~2.5 million actors per GB of heap. Distributed systems without single points of failure. Load balancing and adaptive routing across nodes. Event Sourcing and CQRS with Cluster Sharding. Distributed Data for eventual consistency using CRDTs. Asynchronous non-blocking stream processing with backpressure. -
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Arroyo
Distributed stream processing engine in Rust
Arroyo is a distributed stream processing engine written in Rust, designed to efficiently perform stateful computations on streams of data. Unlike traditional batch processing, streaming engines can operate on both bounded and unbounded sources, emitting results as soon as they are available. -
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CocoIndex
ETL framework to index data for AI, such as RAG
CocoIndex is an open-source framework designed for building powerful, local-first semantic search systems. It lets users index and retrieve content based on meaning rather than keywords, making it ideal for modern AI-based search applications. CocoIndex leverages vector embeddings and integrates with various models and frameworks, including OpenAI and Hugging Face, to provide high-quality semantic understanding. It’s built for transparency, ease of use, and local control over your search data, distinguishing itself from closed, black-box systems. The tool is suitable for developers working on personal knowledge bases, AI search interfaces, or private LLM applications. -
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Lithops
A multi-cloud framework for big data analytics
Lithops is an open-source serverless computing framework that enables transparent execution of Python functions across multiple cloud providers and on-prem infrastructure. 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. -
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Numaflow
Kubernetes-native platform to run massively parallel data/streaming
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. -
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Reactor Core
Non-Blocking Reactive Foundation for the JVM
Reactor Core is a foundational library for building reactive applications in Java, providing a powerful API for asynchronous, non-blocking programming. -
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Riemann
A network event stream processing system, in Clojure
Riemann aggregates events from your servers and applications with a powerful stream processing language. Send an email for every exception in your app. Track the latency distribution of your web app. See the top processes on any host, by memory and CPU. Combine statistics from every Riak node in your cluster and forward to Graphite. Track user activity from second to second. Riemann streams are just functions which accept an event. Events are just structs with some common fields like :host and :service You can use dozens of built-in streams for filtering, altering, and combining events, or write your own. Since Riemann's configuration is a Clojure program, its syntax is concise, regular, and extendable. Configuration-as-code minimizes boilerplate and gives you the flexibility to adapt to complex situations. -
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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. The SageMaker Spark Container is a Docker image used to run batch data processing workloads on Amazon SageMaker using the Apache Spark framework. The container images in this repository are used to build the pre-built container images that are used when running Spark jobs on Amazon SageMaker using the SageMaker Python SDK. The pre-built images are available in the Amazon Elastic Container Registry (Amazon ECR), and this repository serves as a reference for those wishing to build their own customized Spark containers for use in Amazon SageMaker. -
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go-streams
A lightweight stream processing library for Go
A lightweight stream processing library for Go. go-streams provides a simple and concise DSL to build data pipelines. 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. -
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ksqlDB
The database purpose-built for stream processing applications
Build applications that respond immediately to events. Craft materialized views over streams. Receive real-time push updates, or pull current state on demand. Seamlessly leverage your existing Apache Kafka® infrastructure to deploy stream-processing workloads and bring powerful new capabilities to your applications. Use a familiar, lightweight syntax to pack a powerful punch. Capture, process, and serve queries using only SQL. No other languages or services are required. ksqlDB enables you to build event streaming applications leveraging your familiarity with relational databases. Three categories are foundational to building an application: collections, stream processing, and queries. Streams are immutable, append-only sequences of events. They're useful for representing a series of historical facts. Tables are mutable collections of events. They let you represent the latest version of each value per key. -
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protoactor-go
Proto Actor - Ultra fast distributed actors for Go, C# and Java/Kotlin
Built on cloud-native technologies. Taking advantage of proven stability and performance. Asynchronous and Distributed by design. High-level abstractions like Actors and Virtual Grains. Capable of millions of messages per second cross-process communication. Write systems that self-heal using supervisor hierarchies. 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. -
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text-dedup
All-in-one text de-duplication
text-dedup is a Python library that enables efficient deduplication of large text corpora by using MinHash and other probabilistic techniques to detect near-duplicate content. 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. -
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Strings Edit
String editing and formatting library for Ada
Strings edit is a library that provides I/O facilities for integers, floating-point numbers, Roman numerals, and strings. Both input and output subroutines support string pointers for consequent stream processing. The output can be aligned in a fixed size field with padding. Numeric input can be checked against expected values range to be either saturated or to raise an exception. For floating-point output either relative or absolute output precision can be specified. UTF-8 encoded strings are supported, including wildcard pattern matching, sets and maps of code points, upper/lowercase, and other Unicode categorizations. -
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ActiveInsight provides real-time detection and reaction to events and patterns. It is a platform that enables the detection of meaningful events within multiple, high frequency, event streams.
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An innovative Open Source CEP (Complex Event Processing) engine. It implements the event stream processing as a library embeddable in C++ and Perl. You can think of the Complex Event Processing engine as an in-memory database driven by triggers, or a data-flow machine, or a spreadsheet on steroids (and without the GUI part).
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DSPatch
The Refreshingly Simple C++ Dataflow Framework
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 ( https://github.com/MarcusTomlinson/DSPatchables ): A DSPatch component repository. -
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Amadeus
Harmonious distributed data analysis in Rust
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 through a fluent, expressive API and makes it easier to build composable and reliable data transformation pipelines without sacrificing speed or safety. -
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An experimental CEP (Complex Event Processing) engine. It implements the event stream processing as a library embeddable in C++ and Perl. Since then it has been renamed to Triceps, so please look at the new location https://sourceforge.net/projects/t
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Bytewax
Python Stream Processing
Bytewax is a Python framework that simplifies event and stream processing. Because Bytewax couples the stream and event processing capabilities of Flink, Spark, and Kafka Streams with the friendly and familiar interface of Python, you can re-use the Python libraries you already know and love. Connect data sources, run stateful transformations, and write to various downstream systems with built-in connectors or existing Python libraries. Bytewax is a Python framework and Rust distributed processing engine that uses a dataflow computational model to provide parallelizable stream processing and event processing capabilities similar to Flink, Spark, and Kafka Streams. You can use Bytewax for a variety of workloads from moving data à la Kafka Connect style all the way to advanced online machine learning workloads. Bytewax is not limited to streaming applications but excels anywhere that data can be distributed at the input and output. -
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Cosmos DB Spark
Apache Spark Connector for Azure Cosmos DB
Azure Cosmos DB Spark is the official connector for Azure CosmosDB and Apache Spark. The connector allows you to easily read to and write from Azure Cosmos DB via Apache Spark DataFrames in Python and Scala. It also allows you to easily create a lambda architecture for batch-processing, stream-processing, and a serving layer while being globally replicated and minimizing the latency involved in working with big data.
