Are you in a technical role and want to learn the fundamentals of AWS? Do you aspire to have a job or career as a cloud developer, architect, or in an operations role? If so, AWS Cloud Technical Essentials is an ideal way to start. This course was designed for those at the beginning of their cloud-learning journey - no prior knowledge of cloud computing or AWS products and services required! Throughout the course, students will build highly available, scalable, and cost effective application step-by-step. Upon course completion, you will be able to make an informed decision about when and how to apply core AWS services for compute, storage, and database to different use cases. You’ll also learn about cloud security with a review of AWS' shared responsibility model and an introduction to AWS Identity and Access Management (IAM). And, you’ll know how AWS services can be used to monitor and optimize infrastructure in the cloud. AWS Cloud Technical Essentials is a fundamental-level course and will build your competence, confidence, and credibility with practical cloud skills that help you innovate and advance your professional future. Enroll in AWS Cloud Technical Essentials and start learning the technical fundamentals of AWS today! Note: This course was designed for learners with a technical background. If you are new to the cloud or come from a business background, we recommend completing AWS Cloud Practitioner Essentials (https://www.coursera.org/learn/aws-cloud-practitioner-essentials) before enrolling in this course.

Google Cloud Fundamentals for Azure Professionals introduces important concepts and terminology for working with Google Cloud. Through videos and hands-on labs, this course presents and compares many of Google Cloud's computing and storage services, along with important resource and policy management tools.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will work with semi-structured data (ingesting JSON, Array data types) inside of BigQuery. You will practice loading, querying, troubleshooting, and unnesting various semi-structured datasets.

This course provides an introduction to programming frameworks and their implementation issues in the Cloud. It explains multiple topics, including: scalable distributed data stores, resource management (for supporting multi-tenancy and elasticity) and virtualization techniques. Optionally, the student will also be guided in the implementation of a basic version of the distributed runtime system for the Map-Reduce programming framework

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you monitor disk and CPU usage in a Bigtable instance, update an existing cluster to apply node autoscaling, implement replication in an instance, and back up and restore data in Bigtable.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will simulate a real-time real world data set from a historical data set. This simulated data set will be processed from a set of text files using Python and Google Cloud DataFlow, and the resulting simulated real-time data will be stored in Google BigQuery.

This course provides a holistic experience of optimally configuring SAP on Google Cloud. Participants will learn to configure SAP on Google Cloud, and what best practices are, leaving the course with actionable experience to configure SAP on Google Cloud and run SAP workloads on Google Cloud. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<

This is a self-paced lab that takes place in the Google Cloud console. This lab shows how to perform basic operations in Cloud Spanner using the Google Cloud Platform Console. Watch the short video Get a Highly Consistent, Scalable Database Service with Cloud Spanner.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will learn how to leverage Google Cloud Network Tags to route traffic to specific internal TCP/UDP load balancers that frontend separate VM-Series firewalls.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you'll deploy the Autoscaler tool for Cloud Spanner, a companion tool to Cloud Spanner, in the per-project configuration where the autoscaler tools are located in the same project as the Cloud Spanner instance being autoscaled.