Infrastructure Automation for Big Data and Kubernetes

Infrastructure Automation for Big Data and Kubernetes

• 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.

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 and OpenStack Clusters are time-consuming.
• Scaling the infrastructure is complicated due to configuration changes that are required at several places while adding or removing nodes.
• Maintenance of Hadoop and OpenStack clusters 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 extensible platforms to automate OpenStack and Apache Hadoop.
• Solution based on Puppet and Ansible for Configuration Management, Deployment, and 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 triggers. K8 has functionality for pluggable network architectures and a 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, and support for -premises, public, private, and hybrid Cloud environments.

Conforming to existing enterprise security policies. It builds a 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.