Showing 4 open source projects for "event processing"

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    GenStage

    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.
    Downloads: 0 This Week
    Last Update:
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  • 2
    Kestra

    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...
    Downloads: 3 This Week
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  • 3
    memphis

    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...
    Downloads: 5 This Week
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  • 4
    Apache Kafka

    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. ...
    Downloads: 3 This Week
    Last Update:
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