Infrastructure Automation for Big Data and Kubernetes
by Navdeep Singh Gill | 22 February 2017
Table of content
Quick Guide to Infrastructure Automation for Big Data and Kubernetes
Hadoop Cluster with OpenStack enables faster cluster provision and easy configuration. It provides Scale Up and Scales Down by demand supported by various plugins.
Big Data and OpenStack Infrastructure Automation yields -
Built-in security practices to protect data as well as insights.
Distribution of Workload on On-Premises, Private and Public Cloud.
Easier Cluster Monitoring.
Simple Deployment and flexible operation.
Top Challenges for Infrastructure Automation
Infrastructure configuration, provisioning, and deployment on a public Cloud, Private Cloud or Hybrid Cloud.
Automation tools like Puppet and Ansible to automate the Provisioning, Deployment, and Configuration.
Manual Deployment And Scaling of Apache Hadoop, OpenStack Clusters is time-consuming.
Scaling the infrastructure is complicated due to configuration changes which are required at several places while adding or removing nodes.
Maintenance of Hadoop, OpenStack cluster is also cumbersome.
Configuration Management without Automation.
Manual Instance Migrations in OpenStack and Apache Hadoop
DR and Backup for OpenStack and Apache Hadoop.
Infrastructure Automation Solution Offerings
To address the challenges and explore the Infrastructure Automation tools Puppet and Ansible as an extensible platform to automate OpenStack and Apache Hadoop.
Solution based on Puppet and Ansible for Configuration Management, Deployment, provisioning of Apache Hadoop and OpenStack Cluster for On-Premises and Amazon Web Services Cloud.
Automate OpenStack Deployment using Puppet and Ansible.
Automate Apache Hadoop Cluster Deployment using Ansible.
Best Practices for Container Lifecycle Management
Container Management technology has enabled Big Data Pipelines implementation. Serverless Frameworks involving Kubeless and OpenFaas are excellent serverless solutions for easy build and deployment supporting auto-scaling and event trigger. K8 has functionality for pluggable for network architectures and Persistent Volumes Storage feature.It includes Kubernetes on Spark and Kubernetes on HDFS. Requiring full cluster lifecycle management, management of storage and networking resources, integration with existing enterprise services, support for on -premises, public, private and hybrid Cloud environments. Conforming to existing enterprise security policies. It builds Container Orchestration layer to schedule and deploy distributed applications using Docker containers. Offering a wide array of purpose-built features and capabilities for Big Data applications including lifecycle management, multi-tenancy with secure network isolation, support for Kerberos and encrypted HDFS, IOBoost technology for performance optimization, DataTap functionality for compute / storage separation etc.
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