Cloud Computing - Career Transformation Guide (2022 v2)

Colleagues, the new  Cloud Computing - Career Transformation Guide (2022 v2) includes valuable information that enables you to accelerate your career growth and income potential - Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Community and Networking resources. This guide focuses on AWS, Google Cloud Platform, Microsoft Azure and other major cloud providers and technologies. ZipRecruiter currently lists 402,175 cloud-related positions across the United States. Whereas Indeed reports 140,489 alone US-wide. Cloud Engineer openingSalaries tend to rank highest in the greater New York City and San Francisco Bay Area (including Silicon Valley).  Moreover, Indeed estimates the average US salary for Cloud Engineers at $128,837. According to the Global Knowledge 2021 IT Skills and Salary Report four cloud professional certifications rank among the 15 most highly paid positions, including: Rank #2: Google Certified Professional Cloud Architect  ($169,029), Rank #3: AWS Certified Solutions Architect - Associate  ($159,033), Rank #8: Nutanix Certified Professional - Multi Cloud ($142,810), and Rank #12: Microsoft Certified: Azure Administrator Associate ($121,420). 

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Linux and Open Source - Career Transformation Guide

OSS Developers & SysAdmins, the new Linux and Open Source - Career Transformation Guide includes valuable information that enables you to accelerate your career growth and income potential - Career opportunities, Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Forums and Communities. The Certification and Training programs are categorized by Linux Software Development, Linux Networking and Systems Administration, Linux Security, Containers and Git-GitHub. According to ZipRecruiter the average salary for a Linux Software Developer in 2022 is $135,949 (view below). And the average salary for Linux System Administrators in the US is $99,471 with a typical range of $79,204 and $112,193. in 2022 as reported by Salary.com. 

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Introduction to Linux, Open Source Development and GIT - LFD301 (Linux Foundation)

Colleagues, the Introduction to Linux, Open Source Development, and GIT (LFD301) program from the Linux Foundation introduces you to the world of Linux development and gives you the background and training you need to start working with Linux. Linux is exploding, and the demand for Linux developers has never been stronger. If you’ve been thinking about getting into Linux development, this is the best place to start. Learn about the history of Linux and what differentiates it from other UNIX-like operating systems, what the main ingredients of a Linux system are and how to use them, the essentials of system administration, such as user accounts and groups, essential commands, filesystems, security, and more, networking and threading models, aspects of compiling and using libraries, working with Java under Linux. Acquire high-demand skills in Open Source Software, Why Use Open Source Software?, Examples of Successful OSS Projects, How to Work in OSS Projects, Continuous Integration, OSS Licensing and Legal Issues, Leadership vs Control and Why Projects Fail, Respecting and Encouraging Diversity in OSS, GitHub and Other Hosting Providers, Linux and the Operating System, Graphical Environments and Interfaces, System Administration, Text Editors, Shells, bash, and the Command Line, Filesystem Layout, Partitions, Paths and Links, System Initialization, Memory, Networking, Command Details, Users and Groups, Linux Filesystems, Essential Command Line Tools, Bash Scripting, Files and Filesystems, Compiling, Linking and Libraries, Java Installation and Environment, Building RPM and Debian Packages, Introduction to GIT, Git Installation, Git and Revision Control Systems, Using Git: - an Example, Git Concepts and Architecture, Managing Files and the Index, Commits, Branches, Diffs, Merges, Managing Local and Remote Repositories, Using Patches, and Advanced Git Interfaces: Gerrit. {Linux Foundation}

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Graphic source: Linux Foundation

Top 10 Kubernetes & Docker Container Certification & Training Programs (#7)

Colleagues, #7 on our Top 10 countdown is the Certified Kubernetes Application Developer (CKAD) program. It provides a full introduction to containers and Kubernetes; it then moves through the process of creating, managing, and storing applications. The title provides labs for working with the API using curl, as well as using NameSpace and managing pods. Learn how to manage deployments, services, ingress, and setting up storage; and you will get a full walkthrough of key troubleshooting scenarios. The course ends with a sample exam so you can practice before taking the real test. Topics include: 1) Getting Started," explains all you need to know before starting the real CKAD topics: 1) Containers - describing what Kubernetes is, and last, Kubernetes Deployment options and setting up a lab environment are discussed, 2) Kubernetes Essentials," teaches concepts that are extremely important for passing the CKAD exam including the Kubernetes API, 3) Building and Exposing Scalable Applications with ConfigMaps and troubleshooting, and 4) Finally, sample certification exams. {Pearson IT}

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Graphic source: Cloud Native Computing Foundation

Top 10 Kubernetes & Docker Container Certification & Training Programs (#8)

Colleagues, #8 on our Top 10 Container countdown is the Deploying Containerized Workloads Using Google Cloud Kubernetes Engine from Pluralsight. First, you will learn where GKE fits relative to other GCP compute options such as GCE VMs, App Engine, and Cloud Functions. You will understand fundamental building blocks in Kubernetes, such as pods, nodes and node pools, and how these relate to the fundamental building blocks of Docker, namely containers. Pods, ReplicaSets, and Deployments are core Kubernetes concepts, and you will understand each of these in detail. Next, you will discover how to create, manage, and scale clusters using the Horizontal Pod Autoscaler (HPA). You will also learn about StatefulSets and DaemonSets on the GKE. Finally, you will explore how to share states using volume abstractions, and field user requests using service and ingress objects. You will see how custom Docker images are built and placed in the Google Container Registry, and learn a new and advanced feature, binary authorization. When you’re finished with this course, you will have the skills and knowledge of the Google Kubernetes Engine needed to construct scalable clusters running Docker containers on the GCP. Gain high demand, marketable skills including: 1) Introducing Google Kubernetes Engine (GKE) -  Introducing Containers & Kubernetes, Clusters, Nodes, Node Pools, and Node Images, Pods, Kubernetes as an Orchestrator, Replication and Deployment, Services, Volume Abstractions, Load Balancers,  Ingress, StatefulSets and DaemonSets, Horizontal Pod Autoscaler, Pricing; 2) Creating and Administering GKE Clusters - Web Console: Creating a GKE Cluster, Deployments, Exposing a Service, Behind the Scenes Firewall Rules and the Load Balancer, Deleting the Service Deployment and the Cluster, gcloud: Creating a Regional Cluster, Creating and Updating Zonal Clusters, Configuring Custom Node Pools and Resizing Clusters, Using kubectl to Manage Deployments and Expose Services and Update Clusters Delete Services and Deployments, Configure a Horizontal Pod Autoscaler Using kubect, Scaling Clusters Using the Horizontal Pod Autoscaler 3m; 3) Deploying Containerized Workloads to GKE Clusters - Creating a Custom Docker Image, Registering a Custom Image with the Google Container Registry, Deploying a Custom Image and Exposing a Service, Creating and Configuring Ingress Objects, Working with Ingress Objects, Persistent Volume Claims, Deploying and Exposing Multiple Services, Exploring Services Pods Using kubectl, Binary Authorization, Create and Deploy a Custom Test Container, Enable Binary Authorization and Secure Cluster with a Policy,  Create a Note and Associate It with an Attestor, Generate and Associate a Public Key with the Attestor 4m and Deploy Signed Containers; and 4) Monitoring GKE Clusters Using Stackdriver - Monitoring Clusters Using Stackdriver and Prometheus, Enabling Stackdriver Kubernetes Monitoring, Stackdriver Kubernetes Monitoring, Configuring and Viewing Alerts and Prometheus Metrics on Stackdriver.

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Linux Foundation Certified IT Associate (LFCA)

Colleagues,  the average salary for LFCAs is $103,077 according to ZipRecruiter. The Linux Foundation Certified IT Associate (LFCA) exam demonstrates a user’s expertise and skills in fundamental information technology functions, especially in cloud computing. It is ideal for those getting started in an IT career as an administrator/engineer.The LFCA is a pre-professional certification intended for those new to the industry or considering starting an IT career as an administrator or engineer. This certification is ideal for users interested in advancing to the professional level through a demonstrated understanding of critical concepts for modern IT systems including cloud computing. LFCA will test candidates' knowledge of fundamental IT concepts including operating systems, software application installation and management, hardware installation, use of the command line and basic programming, basic networking functions, security best practices, and other related topics to validate their capability and preparedness for an entry-level IT position. Domains and Competencies address: 1) Linux Fundamentals (20%) , 2) System Administration Fundamentals (20%), 3) Cloud Computing Fundamentals (20%), 4) Security Fundamentals (16%), 5) DevOps Fundamentals (16%), and 6) Supporting Applications and Developers (8%). This program includes LFCA Certification Valid for 3 Years, 12 Month Exam Eligibility, Free Retake and Multiple Choice Certification Exam. 

 

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Angular: Zero to Full-Stack Developer Masterclass

Colleagues, the average salary for a Full Stack Developer is $109,735 according to GlassDoor.. The Angular: Zero to Full-Stack masterclass includes over 26+ hours of instruction. Training modules will equip you in: 1) JavaScript, 2) Angular for Modern Web Applications, Node.js Essentials and Full-Stack Web Development with Angular. It provides first-time JavaScript users what they need to become successful developers. Shaun Wassell offers a modern, full-fledged introduction to JavaScript. In particular, the viewer will learn the modern syntax of JavaScript: ES6. Shaun delivers step-by-step guidance that will take the learner from the basic syntax and concepts of JavaScript, all the way through the more advanced concepts that are covered in technical interviews. It also addresses the topics needed to effectively create and manage front-end codebases using Angular, currently one of the most popular front-end JavaScript frameworks. You start with a basic boilerplate application, and throughout the course, add new functionality to your skill set. Angular has undergone some substantial changes over the past few years, and this course presents the latest features and best practices.Node.js Essentials shows developers how to write and run server-side JavaScript applications for the powerful Node.js runtime. Finally, MEAN (MongoDB, Express.js, Angular, and Node.js) is one of the most popular stacks for building full-stack web applications. It enables developers to quickly write an entire application--front end, back end, and database--using only JavaScript. In this course, expert Shaun Wassell demonstrates the MEAN stack through five unique projects.

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What are the Top 10 Cloud Computing Certification & Training Programs? (#6)

Colleagues, the Google Cloud Certified Associate Cloud Engineer (Prep Class &  Practice Test) program from PearsonIT ranks #6 on our Top 10 Countdown. Gain a clear and easily digestible introduction and demonstration of the deployment of applications, monitoring of operations of multiple projects, and maintenance of enterprise solutions to ensure that target performance metrics are met. This video course is also designed to help you study for and pass the Google Cloud Certified Associate Cloud Engineer exam. The learner will gain the knowledge and experience necessary to begin working with public clouds and on-premises solutions. They will be able to use Google Cloud Console and the command-line interface to perform common platform-based tasks to maintain one or more deployed solutions that leverage Google-managed or self-managed services on Google Cloud. Skill-based training modules cover: 1) Introducing Google Cloud Computing - Getting Familiar with Virtualization, Understanding Cloud Computing, Introducing Google Cloud Platform Services, 2) Setting Up a Cloud Solution Environment - Setting Up Cloud Projects and Accounts, Managing Billing Configuration, Installing and Configuring the Command Line Interface, 3) Planning and Configuring a Cloud Solution, Planning and Configuring Compute and Storage, 4) Deploying and Implementing a Cloud Solution - Deploying and Implementing Compute Services, Database and Storage Service, Networking Services and Using GCP Deployment Manager, 5) Ensuring Successful Operation of a Cloud Solution, Monitoring and Logging, Managing Identity and Access Management (IAM).

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What are the Top 10 Cloud Computing Certification & Training Programs? (#7)

Colleagues, #7 on our Top 10 Countdown is Building AI Applications on Google Cloud Platform. Learn about the Cloud AutoML suite of machine learning products that allows developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology. Developers use Cloud AutoML’s graphical user interface to train, evaluate, improve, and deploy models based on their data. Building AI Applications on Google Cloud Platform LiveLessons covers programming components essential to the development of AI and Analytics applications. The focus is on building real-world software engineering applications on the Google Cloud Platform. Several emerging technologies are used to demonstrate the process, including AutoML and Google BigQuery. The Python language is used throughout the course, as Python is becoming the de facto standard language for AI application development in the cloud. Skill-based training modules equip you to: 1) Create an Application Skeleton on GCP using Google App Engine - Set up gcloud environment, Create Hello World application, Deploy Hello World application, Modify and re-deploy Hello World application, Architect Analytics Applications overview; 2) Build ETL (Extract Transform Load) Pipelines - ETL overview, Data flow demo, Cloud functions, GAE Cron jobs, 3) Use ML Prediction on BigQuery - Introduction to Big Theory, Create Supervised ML predictions with BigQuery, Create K-Means Clustering with BigQuery, 4) Use AutoML - AutoML overview, AutoML Vision and Tables. 5) Use AI Platform - Explore AI APIs, Predict with AI Platform, Use Notebooks for Data Science explorations, 6) Build an Analytics Application from Scratch - Create a new Analytics application from scratch, Build queries against BigQuery, Enhance an Analytics application, Enable functionality in an Analytics application, and 7) Using Build Systems and Containers - Architect continuous deployment systems, Configure your repository in Github, Configure makefile in Google Cloud Shell and Build your project in CircleCI.

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Linux Performance Tuning - LFS426 (Linux Foundation)

OSS colleagues, the Linux Performance Tuning program (LFS426) from the Linux Foundation is designed for IT professionals who are responsible for optimizing Linux performance. You will learn about industry configuration best practices, the best tried-and-true optimization performance tuning tools and techniques, how to manually optimize the kernel’s behavior, tracing, profiling and instrumentation techniques across a wide range of conditions. It prepares you to use all the tools and techniques you need to keep your Linux systems running at optimal levels. These techniques and tools have been developed and tested in the most demanding high-performance computing environments. Skill-based training modules include: 1) Performance Optimization Principles, 2) Benchmarking, 3) Tuning Interfaces, 4) Monitoring Interfaces, 5) Profiling Techniques and Tools, 6) Tracing Tools, 7) CPU Subsystem, 8) Power Management, 8) Process Scheduling, 9) Memory Subsystem, 10) NUMA Optimizations, 11) I/O Subsystem, 12) Local Filesystems, 13) Network Filesystems, 14) Storage and IO, 15) Analyzing the I/O Subsystem, 16) Network Subsystem Optimization, and  17) Virtualization.

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What are the Top 10 Cloud Computing Certification & Training Programs? (#8)

Colleagues, the average salary for a Cloud Architect is $133,726 according to GlassDoor. #8 on our Top 10 countdown is the Cloud Native Application Architecture training.  Master the skills necessary to become a successful cloud native architect. Use open source tools and projects like ArgoCD, gRPC and Grafana. Experience with HTTP, Python, Git, Linux, Docker is recommended. Training modules and hands-on projects include: 1) Cloud Native Fundamentals - structure, package, and release an application to a Kubernetes cluster, while using an automated CI/CD pipeline. Students will start by applying a suite of good development practices within an application, package it with Docker and distribute it through DockerHub. This will transition to the exploration of Kubernetes resources and how these can be used to deploy an application. Students will learn the fundamentals of Continuous Integration and Continuous Delivery (CI/CD) with GitHub Actions and ArgoCD and completely automate the release process for an application (Project: TechTrends),  2) Message Passing - Learn how to refactor microservice capabilities from a monolithic architecture, and employ different forms of message passing in microservices. To begin, students will create a migration strategy to refactor a service from a monolith to its own microservice and implement the migration (Project:Refactor UdaConnect), 3) Observability - This course covers the fundamentals of observability in distributed systems. Today, Kubernetes has become the de facto standard for Cloud Native applications and is widely used for distributed systems. Visualize data in a dashboard using Grafana (Project: Building a Metrics Dashboard), and 4) Microservices Security - Learn how to harden a Docker and Kubernetes microservices architecture. To begin, students will learn STRIDE to threat model and reason about microservice security. Next, students will dig deep to explore the Docker and Kubernetes attack surface and be introduced to industry open-source tools such as Docker-bench and Kube-bench to evaluate and harden Docker and Kubernetes weaknesses. Students will then learn about software composition analysis with Trivy and Grype to evaluate image layers and common application security vulnerabilities and provide remediation. Then deploy runtime security monitoring to introspect running microservices for security signals and learn how to respond to a security incident (Project: Hardened Microservices Environment).

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