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https://github.com/infinimesh/plugins

Plugins to connect the infinimesh IoT and AI platform to 3rd party systems like SAP Hana, Snowflake, Elastic, Redis Enterprise and cloud storage of AWS, GCP and Azure
https://github.com/infinimesh/plugins

api golang grafana redis saphana scalytics snowflake time-series time-series-analysis

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Plugins to connect the infinimesh IoT and AI platform to 3rd party systems like SAP Hana, Snowflake, Elastic, Redis Enterprise and cloud storage of AWS, GCP and Azure

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README 

        
# Infinimesh plugins

We publish here plugins to connect infinimesh to external backends. To enable as much as possible modularization we have split the plugins into two main streams:  

* ### generic packages

   * [pkg](pkg)  

   pkg contains shared code to connect to our API, retrieve token and iterate over /objects to find devices in the desired namespace  

   * [redisstream](redistream)  

   shared code for generic cache and stream, based on redis. This package can be included into future plugins.

   

* ### Plugins and connectors

   * [Elastic](Elastic)  

   Connect Infinimesh IoT seamless into [Elastic](https://elastic.co).

   * [Timeseries](timeseries)  

   [Redis-timeseries](https://oss.redislabs.com/redistimeseries/) with [Grafana](https://grafana.com/) for Time Series Analysis and rapid prototyping, can be used in production when configured as a Redis cluster and ready to be hosted via [Redis-Cloud](https://redislabs.com/redis-enterprise-cloud/overview/). 

   * [SAPHana](SAPHana)  

   all code to connect infinimesh IoT Platform to any [SAP Hana](https://www.sap.com/products/hana.html) instance

   * [Snowflake](Snowflake)  

   all code to connect infinimesh IoT Platform to any [Snowflake](https://www.snowflake.com/) instance.  

   * [Cloud Connect](CloudConnect)  

   all code to connect infinimesh IoT Platform to Public Cloud Provider AWS, GCP and Azure. This plugin enables customers to use their own cloud infrastructure and extend infinimesh to other services, like [Scalytics](https://www.scalytics.io), using their own cloud native data pipelines and integration tools. 

  

More plugins will follow, please refer to the plugin directory for any developer friendly documentation.

  

## Building plugins

checkout and build docker based environments starting in the / directory of plugins, like:  

```

git clone https://github.com/infinimesh/plugins.git  

cd plugins  

docker-compose -f timeseries/docker-compose.yml --project-directory . up --build

```

Please read the notes in the different plugin directories how to set ```username``` / ```password``` and API Endpoint (if not using [infinimesh.cloud](https://console.infinimesh.cloud)).  



## Deploy to any Kubernetes / OpenShift  

We recommend to use [kompose](https://kompose.io/) to translate the dockerfiles into kubernetes ready deployments. As example:  

```

# verify that it works via docker-compose  

docker-compose -f Elastic/docker-compose.yml --project-directory . up --build  

  

# convert to k8s yaml  

kompose -f Elastic/docker-compose.yml convert  

  

# prepare env - this makes sure that when we run `docker build` the image is accessible via minikube  

eval $(minikube docker-env)  

  

# build images and change the image name so that the k8s cluster doesn't try to pull it from some registry  

docker build -f ./redisstream/Dockerfile -t redisstream:0.0.1 . # change the image in producer-pod.yaml to redisstream:0.0.1  

docker build -f ./Elastic/Dockerfile -t elastic:0.0.1 . # change the image in consumer-pod.yaml to elastic:0.0.1  

  

# apply each yaml file

kubectl apply -f xxx.yaml  

  

# verify that it's working, eg via logs  

kubectl logs producer  



```