Search Results for "event processing"
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Showing 25 open source projects for "event processing"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Siddhi Core Libraries
Stream Processing and Complex Event Processing Engine
Fully open source, cloud-native, scalable, micro streaming, and complex event processing system capable of building event-driven applications for use cases such as real-time analytics, data integration, notification management, and adaptive decision-making. Event processing logic can be written using Streaming SQL queries via graphical and source editors, to capture events from diverse data sources, process and analyze them, integrate with multiple services and data stores, and publish output to various endpoints in real time. ... -
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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. -
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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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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. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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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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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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GenStage
Producer and consumer actors with back-pressure for Elixir
...Its clear separation of concerns encourages testable, composable stages that can be rearranged as requirements evolve. In production, this leads to predictable, resilient dataflows for event ingestion, batching, and parallel processing. -
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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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EEGLAB
EEGLAB is an open source signal processing environment
EEGLAB is an open source, MATLAB-based interactive environment for analyzing electrophysiological signals such as EEG and MEG. It incorporates powerful tools for data import, preprocessing, independent component analysis (ICA), time-frequency analysis, artifact rejection, and visualization—all within a GUI framework that also supports scripting and plugin extensions. EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and Octave (command... -
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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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Akka
Build concurrent, distributed, and resilient message-driven apps
...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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GridDB
GridDB is a next-generation open source database
...Various architectural innovations, such as in-memory orientation with "memory as the main unit and disk as the secondary unit" and event-driven design with minimal overhead, have been incorporated to achieve processing capabilities that can handle petabyte-scale applications. -
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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.... -
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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... -
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Kestra
Kestra is an infinitely scalable orchestration and scheduling platform
Build reliable workflows, blazingly fast, deploy in just a few clicks. Kestra is an open-source, event-driven orchestrator that simplifies data operations and improves collaboration between engineers and business users. By bringing Infrastructure as Code best practices to data pipelines, Kestra allows you to build reliable workflows and manage them with confidence. Thanks to the declarative YAML interface for defining orchestration logic, everyone who benefits from analytics can participate... -
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elder-pt
data flow framework for data analysis applications
Data analysis in nuclear/particle physics experiments is based on data processing of event-by-event data. Each event contains information measured by arrays of detectors. This data has to be combined, processed, interpreted and reduced in size until the physics information of interest is revealed. Eventually, parts of the data is visualized by sorting it into histograms. Elder-Pt is the prototype of a framework, that facilitates the task of event-by-event data analysis. ... -
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memphis
Next-Generation Event Processing Platform
Memphis enables building modern queue-based applications that require large volumes of streamed and enriched data, modern protocols, zero ops, up to x9 faster development, up to x46 fewer costs, and significantly lower dev time for data-oriented developers and data engineers. Queues and brokers are a mission-critical component in the modern application architecture and should be highly available and stable as possible. Provide great performance while maintaining efficient resource... -
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HStreamDB
HStreamDB is an open-source, cloud-native streaming database
...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 processing. You can use your familiar SQL to perform basic filtering and transformation operations, statistics and aggregation based on multiple kinds of time windows and even joining between multiple streams. With connectors provided, you can easily integrate HStreamDB with other external systems, such as MQTT Broker, MySQL, Redis and ElasticSearch. ... -
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FLOGO
Simplify building efficient & modern serverless functions and apps
Project Flogo is an ultra-light, Go-based open source ecosystem for building event-driven apps. Event-driven, you say? Yup, the notion of triggers and actions are leveraged to process incoming events. An action, a common interface, exposes key capabilities such as application integration, stream processing, etc. All capabilities within the Flogo Ecosystem have a few things in common, they all process events (in a manner suitable for the specific purpose) and they all implement the action interface exposed by Flogo Core. ... -
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ksqlDB
The database purpose-built for stream processing applications
...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. ... -
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Dataflow Java SDK
Google Cloud Dataflow provides a simple, powerful model
The Dataflow Java SDK is the open-source Java library that powers Apache Beam pipelines for Google Cloud Dataflow, a serverless and scalable platform for processing large datasets in both batch and stream modes. This SDK allows developers to write Beam-based pipelines in Java and execute them on Dataflow, taking advantage of features like autoscaling, dynamic work rebalancing, and fault-tolerant distributed processing. While it has been mostly succeeded by the unified Beam SDKs, it remains... -
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TONIC
Analysis of neural spiking data generated by multi-electrode arrays
A neuroscience data analysis package for handling extracellular potentials recorded by multi-electrode arrays, including signal processing, event detection, feature extraction, and spike sorting. -
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Apache Kafka
Mirror of Apache Kafka
Apache Kafka is a distributed streaming platform built around durable, partitioned logs called topics, enabling high-throughput, fault-tolerant event pipelines. Producers append records to partitions, brokers replicate them for durability, and consumer groups read them at their own pace while balancing work across instances. The commit/offset model and retention policies support patterns from real-time processing to event sourcing and audit trails. Exactly-once processing semantics, idempotent producers, and transactions help prevent duplicates across complex dataflows. ... -
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giServer
giServer the easy to use and extensible batch and integration server
The giServer is an easy-to-use integration server for process automation and event-driven or scheduled execution of batch jobs. Instead of using complex XML configuration files an elaborate GUI for batch job management is included. Some possible usage scenarios are: - Automatic processing of incoming data files - Big Data applications - Process automation - Data Mining/Aggregation applications - Automatic Reporting - Processing and analysis of database records -
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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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