Cloud Data Architecture: Data Management & Adoption Frameworks

Cloud Data Architecture: Data Management & Adoption Frameworks

• Course Overview
• recall cloud migration models from the perspective of architectural preferences
• specify prominent big data solutions that can be implemented in the cloud
• recognize the impact of the implementing Kubernetes and Docker in the cloud
• implement Kubernetes on AWS
• implement data management on AWS, GCP, and DBaaS
• implement big data solutions using AWS
• build backup and restore mechanisms in the cloud
• implement disaster recovery planning for cloud applications
• recall prominent cloud adoption frameworks and their associated capabilities
• specify the benefits of implementing Blockchain technologies or solutions in the cloud
• implement Blockchain solutions in the cloud
• implement Kubernetes on AWS, build backup and restore mechanisms on GCP, and implement big data solutions in the cloud

Explore how to implement containers and data management on popular cloud platforms like Amazon Web Services (AWS) and Google Cloud Platform (GCP) for data science. Planning big data solutions, disaster recovery, and backup and restore in the cloud are also covered in this course. Key concepts covered here include cloud migration models from the perspective of architectural preferences; prominent big data solutions that can be implemented in the cloud; and the impact of implementing Kubernetes and Docker in the cloud, and how to implement Kubernetes on AWS. Next, learn how to implement data management on AWS, GCP, and DBaaS; how to implement big data solutions using AWS; how to build backup and restore mechanisms in the cloud; and how to implement disaster recovery planning for cloud applications. Learners will see prominent cloud adoption frameworks and their associated capabilities, and hear benefits of and how to implement blockchain technologies or solutions in the cloud. Finally, learn how to implement Kubernetes on AWS, build backup and restore mechanisms on GCP, and implement big data solutions in the cloud.

Cloud Data Architecture: DevOps & Containerization

Cloud Data Architecture: DevOps & Containerization

• Course Overview
• recognize the impact of implementing containerization on cloud hosting environments
• recall the benefits of container implementation and the role of cloud container services
• describe the concept of serverless computing and its benefits
• describe approaches of implementing DevOps in the cloud
• implement OpsWorks on AWS using Puppet
• classify storage from the perspective of capacity and data access technologies
• specify the benefits of implementing machine learning, deep learning, and artificial intelligence in the cloud
• recognize the impact of cloud technology on BI analytics
• recall container and cloud storage types, container and serverless computing benefits, and advantages of implementing cloud-based BI analytics

In this course, learners discover how to implement cloud architecture for large- scale data science applications, serverless computing, adequate storage, and analytical platforms using DevOps tools and cloud resources. Key concepts covered here include the impact of implementing containerization on cloud hosting environments; the benefits of container implementation, such as lower overhead, increased portability, operational consistency, greater efficiency and better application development; and the role of cloud container services. You will study the concept of serverless computing and its benefits; the approaches of implementing DevOps in the cloud; and how to implement OpsWorks on AWS by using Puppet which provides the ability to define which software and configuration a system requires. See demonstrations of how to classify storage from the perspective of capacity and data access technologies; the benefits of implementing machine learning, deep learning, and artificial intelligence in the cloud; and the impact of cloud technology on BI analytics. Finally, learners encounter container and cloud storage types, container and serverless computing benefits, and advantages of implementing cloud-based BI analytics.