Terraform on GCP (Google Cloud Platform) Training Course

This instructor-led, live training in Taiwan (online or onsite) is aimed at beginner-level to advanced-level DevOps engineers, system administrators, and system architects who wish to enhance their Terraform skills and proficiency in managing complex infrastructure deployments.

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

• Develop and maintain Terraform projects at scale.
• Demonstrate expertise with HCL and some of the more-advanced expression language capabilities and practices.
• Utilize various automation tools and processes for managing Terraform ops in continuous integration and delivery pipelines, including testing and quality assurance tools and processes.
• Act as an expert in any existing or new Terraform project or module.

This instructor-led, live training in Taiwan (online or onsite) is aimed at intermediate-level to advanced-level DevOps engineers, cloud architects, and infrastructure specialists who wish to enhance their skills in managing and orchestrating distributed infrastructure using Terraform's advanced features.

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

• Utilize Terraform's advanced features to create dynamic, scalable infrastructure.
• Structure and manage Terraform modules for optimal reusability and efficiency.
• Integrate Terraform with CI/CD pipelines for seamless automation.
• Debug and troubleshoot complex Terraform configurations effectively.
• Implement best practices for maintaining and scaling cloud infrastructure with Terraform.

This instructor-led, live training in Taiwan (online or onsite) is aimed at engineers who wish to use Terraform to plan and build cloud infrastructure.

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

• Install and configure Terraform.
• Implement an "infrastructure as code" approach to managing private and public cloud environments.
• Write declarative configuration files for more efficient change management and collaboration.
• Improve transparency in the infrastructure procurement process.
• Create, launch, and remove resources across different infrastructure providers (AWS, GCP, Azure, OpenStack, VMware, etc.) from within a single tool.

This instructor-led, live training in Taiwan (online or onsite) is aimed at DevOps engineers with little or no previous experience managing infrastructure. Terraform will be used to automate the setup and deployment of infrastructure on and off the cloud.

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

• Install and configure Terraform.
• Understand the principles of infrastructure as code.
• Set up and automate infrastructure using Terraform.
• Write and share configuration file with team members.

This instructor-led, live training in Taiwan (online or onsite) is aimed at engineers with basic experience in managing infrastructure who wish to use Terraform to design, build, and automate multiple environment configurations and complex DevOps processes.

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

• Set up workspaces for multiple environments with Terraform.
• Know the best practices in managing infrastructure at scale.
• Use the advanced tools and CLI configurations in Terraform.
• Design and develop complex Terraform modules.
• Gain expertise in advanced HCL operators and coding techniques.
• Implement security hardening and code analysis.
• Automate testing processes and CI/CD pipelines.

This instructor-led, live training in (online or onsite) is aimed at engineers who wish to use Terraform on AWS to plan and build cloud infrastructure.

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

• Install and configure Terraform on AWS.
• Implement an "infrastructure as code" approach to managing AWS cloud environments.
• Create, launch, and dismantle infrastructure from within a single tool.
• Write declarative configuration files that can be managed like any other source code in a version control system.
• Quickly update configuration files for effectively responding to changing compute resource needs.
• Collaborate with other infrastructure engineers by sharing configuration files in a common code repository.
• Improve transparency in the infrastructure procurement process.

This instructor-led, live training in Taiwan (online or onsite) is aimed at intermediate-level DevOps engineers who wish to use Terraform to build and manage real infrastructure projects in a cloud environment.

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

• Understand Terraform and IaC fundamentals.
• Learn to write and manage Terraform configurations.
• Master Terraform for infrastructure automation across AWS, Azure, and GCP.
• Utilize Terraform's advanced features including workspaces and modules.
• Integrate Terraform with CI/CD pipelines and Terraform Cloud for team collaboration.

This instructor-led, live training in Taiwan (online or onsite) is aimed at IT professionals who wish to learn the fundamentals of Alibaba Cloud and how to use its cloud computing services.

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

• Understand the Alibaba Cloud solution architecture.
• Learn about the Alibaba Cloud core products and services.
• Understand the capabilities of Alibaba Cloud in dealing with big data and databases.

This instructor-led, live training in Taiwan (online or onsite) is aimed at developers who wish to use Anthos to build, deploy, and optimize applications running on top of multi-cluster Kubernetes infrastructure.

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

• Set up Google Anthos on-premise or in the cloud.
• Define, automate, and enforce policies across different environments.
• Manage traffic between services.
• Use Anthos Service Mesh to monitor, troubleshoot, and improve application performance.
• Implement security controls across all deployments.

Data analysis refers to the process of obtaining, cleaning, and analyzing data, often times in a visual manner, to derive insights for better decision making. More and more data is being stored in databases in the cloud so it is important to have a strategy for understanding, managing and accessing such data. One popular tool for accessing and visualizing data is Power Bi.

This instructor-led, live training (online or onsite) is aimed at technical persons who wish to gain a practical understanding of available cloud solutions, the data analysis processes needed to work with data in the cloud, and the hands-on practice to apply tools such as Power BI to analyze data.

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

• Install and configure Power BI.
• Evaluate the various data solutions offered by cloud providers such as Azure.
• Gain an understanding of the different structures, modeling approaches, and data warehouse designs used to store, manage and access Big Data.
• Apply tools and techniques to clean data in preparation for analysis.
• Build reporting and analytics solutions based on on-premise as well as cloud data.
• Integrate data analytics solutions with a data warehouse.
• Mitigate data security risks and ensure data privacy.

Format of the Course

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

Audience

Software Engineer/Computer Scientists with adequate concepts of algorithms and
familiarity with at least one programming/scripting language but without any experience of Cloud
Computing

Duration

3 days (7 hours / day)

Day 1 - provides end-to-end coverage of fundamental cloud computing topics as they relate to both technology and business. The module is divided into a series of sections, each of which is accompanied by a hands-on exercise.

Day 2 - explores technology-related topics that relate to cloud computing platforms. It does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that address cloud service architecture, cloud security threats and technologies, virtualization and data processing.

Day 3 - provides a technical insight into foundational cloud computing platforms. Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS) environments are explored as compound patterns, comprised of unique and shared building blocks. This module is structured as a guided tour through these architectural layers, describing primary components, highlighting shared components and explaining how building blocks can be assembled and implemented via cloud computing mechanisms and practices

Day 4 - builds upon Day 3 to provide a deep dive into elastic, resilient and multitenant technology architectures, as well as specialized solution architectures, such as cloud bursting and cloud balancing. Through the study of architectural mechanisms, industry technologies and design patterns, both core and extended components are described that combine to realize elasticity, resiliency and multitenancy as primary characteristics of cloud platforms. By leveraging these native and enhanced scalability and failover-related feature-sets, specialized solution architectures are described to enable bursting between clouds and on-premise and cloud environments, as well as the balancing of runtime loads across clouds for performance and failover purposes.

Day 5 - presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered previously.

This course has been created for people who want to understand how to benefit from cloud computing. It uses Amazon EC2 example, but can be tailored to other providers.

Audience

IT architects, mid-level IT managers, IT consultants

Format of the course

Currently 100% lectures.

This instructor-led, live training in Taiwan (online or onsite) is aimed at technical persons who wish to gain a practical understanding of available cloud solutions, the data analysis processes needed to work with data in the cloud, and the hands-on practice to apply tools such as Power BI to analyze data.

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

• Install and configure Power BI.
• Evaluate the various data solutions offered by cloud providers such as Azure.
• Gain an understanding of the different structures, modeling approaches, and data warehouse designs used to store, manage and access Big Data.
• Apply tools and techniques to clean data in preparation for analysis.
• Build reporting and analytics solutions based on on-premise as well as cloud data.
• Integrate data analytics solutions with a data warehouse.
• Mitigate data security risks and ensure data privacy.