nifikop
DISCONTINUED
helm-nifi
Our great sponsors
| nifikop | helm-nifi | |
|---|---|---|
| 1 | 2 | |
| 118 | 176 | |
| - | 4.0% | |
| 7.7 | 1.6 | |
| about 1 year ago | about 2 months ago | |
| Go | JavaScript | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
nifikop
-
Nifi on EKS CLUSTER
I don't have experience of Nifi, but I'd say your best option is either the operator: https://github.com/Orange-OpenSource/nifikop or the helm chart: https://github.com/cetic/helm-nifi
helm-nifi
-
Nifi on EKS CLUSTER
I don't have experience of Nifi, but I'd say your best option is either the operator: https://github.com/Orange-OpenSource/nifikop or the helm chart: https://github.com/cetic/helm-nifi
What are some alternatives?
helmify - Creates Helm chart from Kubernetes yaml
cluster-example - Terraform cluster example with Druid, Kafka and NiFi
argocd-operator - A Kubernetes operator for managing Argo CD clusters.
helm-charts - A curated set of Helm charts brought to you by codecentric
helm-charts - Helm charts for Vector.
charts - Bitnami Helm Charts
helm-charts - Prometheus community Helm charts
db-operator - The DB Operator creates databases and make them available in the cluster via Custom Resource.
rbacsync - Automatically sync groups into Kubernetes RBAC
kubernetes-operator-roiergasias - 'Roiergasias' kubernetes operator is meant to address a fundamental requirement of any data science / machine learning project running their pipelines on Kubernetes - which is to quickly provision a declarative data pipeline (on demand) for their various project needs using simple kubectl commands. Basically, implementing the concept of No Ops. The fundamental principle is to utilise best of docker, kubernetes and programming language features to run a workflow with minimal workflow definition syntax. It is a Go based workflow running on command line or Kubernetes with the help of a custom operator for a quick and automated data pipeline for your machine learning projects (a flavor of MLOps).