Showing 4 open source projects for "seismic data processing software"

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  • 1
    Numaflow

    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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  • 2
    kube-fledged

    kube-fledged

    Kubernetes operator to create and manage cache of container images

    kube-fledged is a kubernetes operator for creating and managing a cache of container images directly on the worker nodes of a Kubernetes cluster. It allows a user to define a list of images and onto which worker nodes those images should be cached (i.e. pulled). As a result, application pods start almost instantly, since the images need not be pulled from the registry. kube-fledged provides CRUD APIs to manage the lifecycle of the image cache, and supports several configurable parameters to...
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  • 3
    Loggie

    Loggie

    A lightweight, cloud-native data transfer agent and aggregator

    Loggie is a lightweight, high-performance, cloud-native agent and aggregator based on Golang. Loggie includes LogConfig/ClusterLogConfig/Interceptor/Sink CRDs, allowing for the creation of data collection, transfer, processing, and sending pipelines through simple YAML file creation. Supports deployment as an independent intermediate machine, which can receive aggregated data sent by Loggie Agent and can also be used to consume and process various data sources. Configure Filebeat and Loggie...
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  • 4
    Brigade

    Brigade

    Event-driven scripting for Kubernetes

    Brigade is a full-featured, event-driven scripting platform built on top of Kubernetes. It integrates with many different event sources, more are always being added, and it's easy to create your own if you need something specific. The best part is that Kubernetes is well-abstracted so even team members without extensive Kubernetes experience or without direct access to a cluster can be productive.
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