MinIO Cloud Storage Stack ( minio | 14 hours )

• Experience with shell scripting

Audience

• Cloud Engineers

MinIO is a cloud based storage server for storing objects and unstructured data. With MinIO, users are able to build high performance infrastructures that are lightweight and scalable.

This instructor-led, live training (online or onsite) is aimed at cloud engineers who wish to store objects and unstructured data using MinIO.

By the end of this training, participants will be able to:

• Provide an alternative to Unix Commands with the MinIO Client.
• Use MinIO to build high performance infrastructures for machine learning, analytics, and more.
• Deploy MinIO on Kubernetes for orchestrated deployment to scale.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction

MinIO Object Storage

• Scalabilty
• Cloud Native
• Amazon S3 compatibility

MinIO Features and Architechture

• Erasure encoding
• Encryption
• Continuous replication
• Multi-cloud gateway

Preparing the Development Environment

• Installing and configuring MinIO
• Installing and configuring Hortonworks Data Platform
• Installing and configuring Spark
• Installing and configuring MinIO Client
• Testing with MinIO Client

MinIO Server

• Running MinIO Server with erasure code
• Passing drive locations to start a distributed instance
• Expanding an existing distributed setup
• Running sample applications
• Securing access with TLS
• Adding endpoints
• Enabling bucket notification
• Migrating config and TLS certificates
• Setting up configurations
• Hosting multiple tenants

MinIO Client

• Running MinIO Client
• Adding a cloud service storage
• Understanding the MinIO Client Commands
• Adding shell aliases

MinIO Deployment with Kubernetes

• Creating and updating distributed MinIO clusters with MinIO Operator
• Using Helm Chart
• Deploying with YAML files

Summary and Conclusion