MinIO is a cloud-native application designed to scale in a sustainable manner in multi-tenant environments. Orchestration platforms provide perfect launchpad for MinIO to scale. Below is the list of MinIO deployment documents for various orchestration platforms:

# MinIO Batch Job
MinIO Batch jobs is an MinIO object management feature that lets you manage objects at scale. Jobs currently supported by MinIO

- Replicate objects between buckets on multiple sites

Upcoming Jobs

- Copy objects from NAS to MinIO
- Copy objects from HDFS to MinIO

## Replication Job
To perform replication via batch jobs, you create a job. The job consists of a job description YAML that describes

### Creating the Scale-out Backup Repository

- Under the Backup Infrastructure view, click on Scale-out Repositories and click the Add Scale-out Repository button on the ribbon.

- Follow the on screen wizard

MinIO is a high performance distributed object storage server, designed for large-scale private cloud infrastructure. MinIO is designed in a cloud-native manner to scale sustainably in multi-tenant environments. Orchestration platforms like Kubernetes provide perfect cloud-native environment to deploy and scale MinIO.

## MinIO Deployment on Kubernetes

There are multiple options to deploy MinIO on Kubernetes:

1. [Standalone Deployment](#standalone-deployment)
2. [Distributed Deployment](#distributed-deployment)
3. [Cloud Scale Deployment](#cloud-scale-deployment)

## 1. Standalone Deployment

To host multiple tenants on a single machine, run one MinIO Server per tenant with a dedicated HTTPS port, configuration, and data directory.

Since the domain certificates are stored in the gateway namespace, it is recommended to keep each
gateway in a dedicated namespace and restrict access.

For large-scale gateways it is optionally possible to use a dedicated pilot in the gateway namespace.

### Additional test templates

A number of helm test setups are general-purpose and should be installable in any cluster, to confirm

* [Building Groovy Libraries](https://docs.gradle.org/current/samples/sample_building_groovy_libraries.html)
* [Building Scala Applications](https://docs.gradle.org/current/samples/sample_building_scala_applications.html)
* [Building Scala Libraries](https://docs.gradle.org/current/samples/sample_building_scala_libraries.html)
* [Building Kotlin JVM Applications](https://docs.gradle.org/current/samples/sample_building_kotlin_applications.html)

streaming compression due to its stability and performance.

This algorithm is specifically optimized for machine generated content.
Write throughput is typically at least 500MB/s per CPU core,
and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

This means that in cases where raw IO is below these numbers