Top 15 Google Cloud Platform (GCP) Courses & Certifications Online in 2024

Google Cloud platform is one of the top 3 public cloud providers today. Google jumped on the cloud bandwagon a little late in 2008 with the launch of Google App Engine. But has since then changed the public cloud landscape. It still lags behind Amazon’s AWS and Microsoft’s Azure when it comes to market share, but is swiftly catching up. GCP is particularly gaining ground in the Data Analytics and Machine Learning field. It was recently able to score Spotify and Apple from Amazon. Several top companies including Twitter, 20th Century Fox, The New York Times, PlanetLabs, Evernote have opted for Google cloud over other service providers.

Public Cloud Computing market has witnessed massive growth in the past decade. In 2018, it was around 141 billion US dollars making Cloud Computing one of the most in-demand technical skill of 2019. Hence, there is a massive demand for cloud computing professionals with expertise in Google Cloud Platform products and tools. On top of it, there is a shortage of skilled GCP developers in the industry, which makes GCP skills even more important. For anyone who wishes to advance their career in cloud computing, adding Google Cloud skills to the resume will be worth an investment. So, if you are looking to master Google Cloud and establish yourself in the cloud industry, now is the best time to do so.

The best way to learn Google cloud is to take an online GCP course. Coursera and Udemy are leaders in providing best online Google cloud courses that help you learn anytime and from anywhere. Most of these certified GCP courses are being officially offered by Google itself on these platforms. We have compiled a list of best Google Cloud Platform courses, certifications, trainings, tutorials and classes that you take online in 2024. These include resources for both beginner and intermediate level learners that will equip you with concepts, tools and products that make up the world of google cloud. You will learn about Google’s offerings for developers, data architects, machine learning professionals etc.

Some of these online GCP trainings will also prepare you for Google certifications like Google Cloud Certified Associate Cloud Engineer, Professional Cloud Architect, Professional Data Engineer and Professional Cloud Developer and more.

List of Best 15 Google Cloud Platform Courses & Certifications Online in 2021

1. Architecting with Google Compute Engine Specialization by Google Cloud (Coursera)

Google Cloud Platform is used in a wide variety of environments, all the way from startups to global enterprises. This google cloud architect certification is designed to familiarize learners with the various components of Google Cloud Platform and prepare them to implement solutions using Google Cloud Platform in any of these types of environments.

There are 5 courses in this specialization that provide learners with an understanding of the comprehensive and flexible infrastructure and platform services provided by Google Cloud Platform, with a focus on Compute Engine. This Specialization used to be called Architecting with Google Cloud Platform. But now the Specialization is focused more on Compute Engine as the computing platform, so Google has renamed it to Architecting with Google Compute Engine.

There is a plethora of demos, presentations, hands-on labs and exercises, through which participants will learn how to deploy various solution elements and infrastructure components such as networks, systems and applications services. The five courses in this Google Compute Specialization include the following:

1. Google Cloud Platform Fundamentals: Core Infrastructure – Here you will learn about, and compare, many of the computing and storage services available in Google Cloud Platform, including Google App Engine, Google Compute Engine, Google Kubernetes Engine, Google Cloud Storage, Google Cloud SQL, and BigQuery.
2. Essential Google Cloud Infrastructure: Foundation – Here you will be introduced to infrastructure design and virtual networking configuration with Virtual Private Cloud (VPC), Projects, Networks, Subnetworks, IP addresses, Routes, and Firewall rules. You will also learn how to use the Google Cloud Platform through the console and Cloud Shell.
3. Essential Google Cloud Infrastructure: Core Services – This course covers deploying practical solutions including customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring.
4. Elastic Google Cloud Infrastructure: Scaling and Automation – Here you will learn how to deploy solution elements, including securely interconnecting networks, load balancing, autoscaling, infrastructure automation and managed services.
5. Reliable Google Cloud Infrastructure: Design and Process – Here you will learn to design Google Cloud deployments that are highly reliable, highly available, secure, and cost-effective.

This specialization focuses on applied learning and therefore incorporates hands-on labs using Qwiklabs platform. These give students an opportunity to apply the skills learnt in the video lectures and gain practical hands-on experience with the concepts explained throughout the modules.

This google cloud training also helps learners prepare for the Google Cloud Associate Cloud Engineer Certification. Learners are expected to have basic proficiency with command-line tools and Linux operating system environments and systems operations experience with deploying and managing applications.

Key Highlights

• Understand the infrastructure components and application services that form a part of Google Cloud Platform
• Understand how to assess tradeoffs and make sound choices among Google Cloud Platform products
• Learn to integrate on-premises and cloud resources
• Gain practical hands-on experience using Qwiklabs platform
• Earn a Specialization Certificate to share with your professional network and potential employers
• All participants who complete this specialization will be able to share their information directly with Google and partners to be considered for open hiring opportunities

Duration : 4-5 weeks, 6-10 hours per week
Rating : 4.7
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2. G Suite Administration Specialization by Google Cloud (Coursera)

The G Suite Administration Specialization aims to help system administrators master G Suite so that they are better able to manage and establish G Suite best practices for their organization.

The specialization assumes little or no knowledge of G Suite. It walks you through what G Suite is and how the basics work. It is a very comprehensive course and packs a lot of information in the form of short, structured, modular sessions. Along with a series of readings, there are several step-by-step hands-on exercises, and knowledge checks that help learners gain the necessary skills to get started and successfully work as G Suite administrators.

This Google cloud certification is focussed on applied learning and is structured as following 4 courses:

1. Introduction to G Suite – In this course, you will setup and configure a new G Suite account, and explore options for provisioning users, groups and resources. You will learn how to manage your users and also become familiar with organizational structures. You will be introduced to your Cloud Directory and will learn how to split your organization into organizational units to simplify user and service management. Finally you will learn how to delegate admin privileges to other users in your organization.
2. Managing G Suite – This course focuses on the G Suite core services such as Gmail, Calendar, and Drive & Docs. You will learn to configure these services to meet your particular business needs. You will also learn about Google Vault, G Suite admin console reports, setting up administrator alerts, multi domain management within G Suite and more.
3. G Suite Security – In this security course you will be introduced to Google’s best practices to protect your users and data. You will examine user and application security and become familiar with the Single Sign On (SSO) options available for your organization. You will be able to use the tools provided to identify security events and risks and mitigate problems that may arise.
4. G Suite Mail Management – In this course you will learn how to protect your organization against spam, spoofing, phishing and malware attacks. You will configure email compliance and learn how to implement data loss prevention (DLP) for your organization. You will gain an understanding of the mail routing options available and learn how to whitelist and block senders. You will also become familiar with other mail options such as inbound and outbound gateways, 3rd party email archiving, and journaling to Vault.

Key Highlights

• Perfect course for beginners who have never used G Suite before
• Learn common administrative tasks in the G Suite admin console and advise on best practices for organizational structure, user, and service management
• Describe Google Vault and learn how to use it to retain, search and export your organization’s data
• Learn to monitor security events and risks and put in place the necessary measures to protect your users and organizational data
• Implement fundamental best management practices behind various G Suite services
• Implement common email security measures in your DNS records such as SPF, DKIM and DMARC and be able to explain the purpose of each measure

Duration : 4 weeks, 16 hours per week
Rating : 4.8
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3. Machine Learning with TensorFlow on Google Cloud Platform Specialization (Coursera)

Machine Learning and Cloud are driving major transformations across several industries today. A wide variety of environments, all the way from start-ups to global enterprises are observing a huge demand for cloud computing professionals who can build machine learning models. This specialization is part one of two Machine Learning specializations that have been designed by Google to help prepare learners to implement machine learning solutions using Google Cloud Platform in many of these types of environments.

This Google Cloud machine learning certification is great for anyone interested in or already working in machine learning. Here you will learn what Machine learning is and through the qwiklabs, you’ll practice what you learn in video lectures. The content is concise and well-delivered. While there are many theoretical machine learning courses, this specialization provides an interactive and practical way to learn ML quickly and effectively. There are several open-source, example TensorFlow applications included in the courses that you can take and deploy immediately to get a jump-start.

The 5 courses in the specialization are as follows:

1. How Google does Machine Learning – This course provides an introduction to machine learning and the kind of problems it solves. It discusses Google’s strategy of AI-first and what that means in practice. You will also learn to set up Python notebooks in the cloud, called Cloud Datalab, which is the development environment used in this specialization.
2. Launching into Machine Learning – This course discusses why neural networks are so popular in a variety of data science problems, how to set up a supervised learning problem and find a good solution using gradient descent, how to optimize and evaluate models using loss modes and performance metrics. You’ll also learn to create repeatable and scalable training, evaluation, and test datasets.
3. Intro to TensorFlow – In this course you will learn the core components of TensorFlow and get hands-on practice building machine learning programs. You will work through the necessary concepts and APIs to be able to write distributed ML models. You will learn to use the TensorFlow libraries to solve numerical problems, troubleshoot and debug common TensorFlow code pitfalls and also understand how to deploy and productionalize ML models at scale with Cloud ML Engine.
4. Feature Engineering – In this course, you will learn how to improve the accuracy of your models, and find which data columns have features that are most useful and how to pre-process and transform them for optimal use in your ML models.
5. Art and Science of Machine Learning – In this data science course you will learn the essential skills of ML intuition, good judgment and experimentation to finely tune and optimize your ML models for the best performance. You will learn how to manually configure the models to see the impact on model performance. And then automatically tune them with hyperparameters using cloud machine learning engine on Google Cloud Platform.

Once you complete this specialization, it is recommended that you continue to Part 2 – Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization. The full 10-course journey takes learners from a strategic overview of why ML matters all the way to building custom sequence models and recommendation engines.

Key Highlights

• Learn why Machine Learning matters and what kinds of problems it can solve
• Learn to build machine learning models that meaningfully impact the businesses and the customers they serve
• Learn practical tips and pitfalls from Machine Learning practitioners at Google
• Content delivered with applied learning approach to help students gain new skills with hands-on labs
• Experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform
• Opportunity to add machine learning projects to your resume

Duration : 4-5 weeks, 16 hours per week
Rating : 4.6
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4. Networking in Google Cloud Platform Specialization by Google Cloud (Coursera)

This Google Cloud Networking certification aims to provide learners with a broad understanding of core infrastructure and networking options on Google Cloud Platform. It has been designed for Network Engineers and Admins who are either using Google Cloud Platform or are planning to do so. It is suitable for anyone looking to learn software-defined networking solutions in the cloud. As a participant of this specialization, you will explore and deploy GCP networking technologies, such as Google Virtual Private Cloud (VPC) networks, subnets, firewalls; interconnection among networks; load balancing; Cloud DNS; Cloud CDN. The specialization also covers common network design patterns and automated deployment using Deployment Manager.

This is an intermediate level specialization and thus it is expected that students undertaking it have a prior understanding of the OSI 7-layer model and IPv4 addressing and some experience with managing IPv4 routes.

The content of this GCP network certification is delivered as 3 following courses:

1. Google Cloud Platform Fundamentals: Core Infrastructure – In this course students learn about and compare, many of the computing and storage services available in Google Cloud Platform, including Google App Engine, Google Compute Engine, Google Kubernetes Engine, Google Cloud Storage, Google Cloud SQL, and BigQuery. Resource and policy management tools, such as the Google Cloud Resource Manager hierarchy and Google Cloud Identity and Access Management are also explored.
2. Networking in Google Cloud: Defining and Implementing Networks – This course covers Google Virtual Private Cloud (VPC) networks, subnets and firewalls, access control to networks, sharing networks and load balancing.
3. Networking in Google Cloud: Hybrid Connectivity and Network Management – This course covers interconnection among networks, common network design patterns and the automated deployment of networks using Deployment Manager or Terraform. Students also learn about networking pricing and billing to help them optimize network spend and monitoring and logging features that can help troubleshoot Google Cloud network infrastructure.

In addition to the recorded lectures, there is a plethora of demonstrations, and hands-on labs that help students apply the learning from the video lectures and gain foundational skills for working with GCP. The hands-on labs are incorporated using Qwiklabs platform.

Key Highlights

• Design, develop, and manage cloud networking solutions to drive business objectives
• Learn to Configure Google VPC networks, subnets, and routers
• Control administrative access to VPC objects
• Learn about Google Cloud load balancer and proxy options and configure them

Duration : 1 month, 15 hours per week
Rating : 4.8
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5. Developing Applications with Google Cloud Platform Specialization by Google Cloud (Coursera)

This Coursera google cloud developer specialization is intended for application developers who are looking to learn how to build cloud-native applications from scratch or redesign existing applications to run on Google Cloud Platform. Through 4 courses, this specialization teaches them the design, development and deployment of applications that seamlessly integrate managed services from the Google Cloud Platform (GCP).

This is a very hands-on GCP training with a series of presentations, demos and hands-on labs, through which the students learn key GCP concepts and services like compute engine, cloud storage, dataflow, etc and how to use these GCP services with pre-trained machine learning APIs to develop secure, scalable and intelligent cloud-native applications.

Since it is an intermediate level specialization, it is assumed that the participants have prior working knowledge of Node.js and basic proficiency with command-line tools and Linux operating system environments. The 4 courses in the specialization cover the following:

1. Google Cloud Platform Fundamentals: Core Infrastructure – This course is a part of several certifications and specializations offered by Google Cloud on Coursera. It orients learners to the basics of Google Cloud Platform and the four main kinds of services offered by GCP i.e. Compute, Storage, Big Data, and Machine Learning. It also discusses important Google VPC networking capabilities and Google Cloud Identity and Access Management.
2. Getting Started With Application Development – This course introduces learners to the Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK. The participants learn how to apply best practices for application development and use the appropriate GCP storage services for object storage, relational data, caching, and analytics.
3. Securing and Integrating Components of your Application – This course teaches how to develop more secure applications, implement federated identity management, and integrate application components by using messaging, event-driven processing, and API gateways. The participants are introduced to Cloud Pub/Sub, pre-trained machine learning APIs, Cloud functions and Cloud end-points etc.
4. App Deployment, Debugging, and Performance – This course covers deploying your applications in GCP, the various execution environments available for your application in GCP, and how to debug, monitor, and trace performance in your applications using Stackdriver.

The specialization is expected to be completed in one month time, provided a student spends 14 hours per week. But since it is available completely online, one can approach the subjects at one’s own pace. It is also available in French, German, Spanish, and Japanese apart from English.

Key Highlights

• Be able to identify the purpose and value of Google Cloud Platform products and services
• Describe best practices for cloud-native application development
• Choose the appropriate data storage option for application data
• Learn to develop loosely coupled application components or microservices
• Learn to deploy applications using Container Builder, Container Registry, and Deployment Manager
• Implement federated identity management using Firebase authentication
• Learn to choose the appropriate application runtime environment; use Google Kubernetes Engine as a runtime environment and later switch to a no-ops solution with Google App Engine flexible environment

Duration : 1 month, 14 hours per week
Rating : 4.6
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6. Cloud Architecture with Google Cloud Professional Certificate (Coursera)

The Google Cloud Professional Cloud Architect certification is the highest paying IT certification of 2019. This certification was launched by Google in year 2017 and it quickly rose to the top of the list of highest paying certifications in the field of IT. The Cloud Architecture certificate program offered by Google on Coursera is a great pathway to train for this industry-recognised Google’s Professional Cloud Architect certification exam.

This program provides the students with necessary skills needed to advance their career in cloud architecture. It teaches how to deploy solution elements, including infrastructure components such as networks, systems and applications services.

This GCP Professional Certificate is structured as 6 courses, which cover the following concepts and topics in detail:

• The computing and storage services available in Google Cloud Platform, including Google App Engine, Google Compute Engine, Google Kubernetes Engine, Google Cloud Storage, Google Cloud SQL, and BigQuery
• Resource and policy management tools, such as the Google Cloud Resource Manager hierarchy and Google Cloud Identity and Access Management
• Infrastructure components such as networks, virtual machines and applications services
• Using the Google Cloud Platform through the console and Cloud Shell
• How to deploy practical solutions including customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring
• How to deploy solution elements, including securely interconnecting networks, load balancing, autoscaling, infrastructure automation and managed services
• Architect cloud and hybrid networks
• How to choose the appropriate Cloud Storage services based on application requirements
• How to choose the right Google Cloud deployment services for your applications
• Implementing reliable, scalable, resilient applications balancing key performance metrics with cost
• Securing cloud applications, data, and infrastructure
• Monitoring service level objectives and costs using Google Cloud tools

The content of the courses is very informational and also contains hands-on exercises with Qwiklabs. These hands-on labs help students to understand the concepts and knowledge in depth. This makes the video lectures more relevant and courses more engaging.

Key Highlights

• Learn the skills needed to be successful in a cloud architect role
• Prepare for the Google Cloud Professional Cloud Architect certification
• Test your skills and abilities with Activity Tracking Challenge Labs
• Graded practice exam quiz that simulates the exam-taking experience
• Review each section of the exam using highest-level concepts to identify what is already known and surface gap areas for study
• Gain real-world experience through a number of hands-on Qwiklabs projects that you can share with potential employers

Duration : 6 weeks, 5 hours per week
Rating : 4.7
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7. From Data to Insights with Google Cloud Platform Specialization by Google Cloud (Coursera)

This Specialization teaches how to derive insights through data analysis and visualization using the Google Cloud Platform. It is designed for Data Analysts, Business Analysts, Business Intelligence Professionals and Cloud Data Engineers who are involved in building scalable data solutions on GCP and want to learn about data analysis that scales automatically as the data grows.

Apart from video lectures, there are multitudes of interactive scenarios and hands-on labs where students get to explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. Through the course of the specialization topics of data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization are also discussed.

This GCP certificate is delivered as a series of 4 following courses:

1. Exploring and Preparing your Data with BigQuery – This course discusses the key big data tools on Google Cloud Platform, so students become familiar with using BigQuery and Cloud Dataprep to analyze and transform datasets. Participants also learn how to assess the quality of their datasets and develop an automated data cleansing pipeline that will output to BigQuery.
2. Creating New BigQuery Datasets and Visualizing Insights – This course covers how to ingest new external datasets into BigQuery and visualize them with Google Data Studio. Students also learn to compare data visualizations and SQL concepts like multi-table JOINs and UNIONs which allows analysis of data across multiple data sources.
3. Achieving Advanced Insights with BigQuery – This course deepens students knowledge of SQL on BigQuery by covering about more advanced functions like statistical approximations, analytical window queries, user-defined functions, and WITH clauses. Also advanced visualization topics like dashboard calculated fields, filters, multi-page reports, and dashboard cache are explored.
4. Applying Machine Learning to your Data with GCP – This course discusses what Machine Learning is and how it can benefit businesses. Students learn how to create machine learning models directly inside of BigQuery. They also learn the new syntax and work through the phases of building, evaluating, and testing an ML model.

This is a beginner level specialization, so no prior experience is required. But, it is recommended that participants have knowledge of ANSI SQL to get the most out of this specialization.

Key Highlights

• Understand the common challenges faced by data analysts and how to solve them with the big data tools on Google Cloud Platform
• Understand how pricing works in BigQuery and how you can best optimize your queries
• Get to practice writing and troubleshooting SQL on a real Google Analytics e-commerce dataset to drive marketing insights
• Learn Cloud Datalab (a key tool in the Data Scientist toolkit) which enables analysts to collaborate through the use of scalable cloud notebooks
• Learn about optimizing your queries for performance and how you can secure your data through authorized views
• Practice using the pretrained Machine Learning APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML

Duration : 2 months, 6 hours per week
Rating : 4.7
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8. Security in Google Cloud Platform Specialization by Google Cloud (Coursera)

Securing systems is a hot topic and is becoming a priority for everyone today. This google cloud security certification is designed to give participants a broad study of security controls and techniques on Google Cloud Platform to help them prepare for implementing security solutions using GCP. It discusses how to deploy the components of a secure GCP solution, including Cloud Identity, the GCP Resource Manager, Cloud IAM, Google Virtual Private Cloud firewalls, Google Cloud Load balancing, Cloud CDN, Cloud Storage access control technologies, Stackdriver, Security Keys, Customer-Supplied Encryption Keys, the Google Data Loss Prevention API, and Cloud Armor. Participants also learn mitigations for attacks at many points in a GCP-based infrastructure, including Distributed Denial-of-Service (DDOS) attacks, phishing attacks, and threats involving content classification and use.

The specialization comprises of 3 courses delivered as recorded lectures, demonstrations, and hands-on labs:

1. Google Cloud Platform Fundamentals: Core Infrastructure – This course is a part of multiple GCP specializations and professional certificates. It teaches the basics of Google Cloud Platform with a focus on computing and storage services available in Google Cloud Platform, including Google App Engine, Google Compute Engine, Google Kubernetes Engine, Google Cloud Storage, Google Cloud SQL, and BigQuery. It also discusses the available big-data and machine learning services.
2. Managing Security in Google Cloud Platform – This course introduces participants to GCP’s approach to security. It discusses the shared security responsibility model (which is a collaborative effort between Google and its users), access transparency, cloud identity, the Google Cloud Directory Sync and Single Sign-On, the resource manager, Stackdriver monitoring and logging, cloud audit logging and more.
3. Mitigating Security Vulnerabilities on Google Cloud Platform – In this course participants learn about security tools available to them when using GCP, and how to implement security “best practices” to lower the risk of malicious attacks against their systems, software and data. It covers topics like securing compute engine, securing cloud data and managing DDoS attacks.

This is an intermediate level specialization intended for Cloud information security analysts, architects, engineers, Info security/cybersecurity specialists and infrastructure architects. It is expected that participants have knowledge of foundational concepts in information security and systems operations experience with deploying and managing applications, on-premises or in a public cloud environment and some basic knowledge of Python or Javascript.

Key Highlights

• Understand the Google approach to security
• Manage administrative identities using Cloud Identity and Implement IP traffic controls using VPC firewalls and Cloud Armor
• Implement least privilege administrative access using Google Cloud Resource Manager, Cloud IAM
• Learn how to leverage Forseti Security to systematically monitor your GCP resources
• Remediate important types of vulnerabilities, especially public access to data and VMs
• Understand encrypting persistent disks with Customer Supplied Encryption keys
• Opportunity to practice what you’ve learned, by completing the labs exercises. Some examples include “Configuring VPC Firewalls” and “Using and Viewing VPC Flow Logs in Stackdriver”.

Duration : 2 months, 16 hours per week
Rating : 4.7
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9. Architecting with Google Kubernetes Engine Specialization by Google Cloud (Coursera)

IT Organizations world over want the capability to deploy software fast, efficiently and scale big. Kubernetes, containers, and Google Kubernetes Engine (GKE) can help you do that. This google kubernetes training helps you learn these technologies as it is focused on building efficient computing infrastructures using Kubernetes and GKE. Kubernetes is arguably the most important open-source, vendor-neutral container management technology in the world today. In this specialization, you will gain an understanding of how you can run Kubernetes, and deploy production solutions on it, using Google Cloud Platform (GCP). You’ll also learn about Google Kubernetes Engine (GKE) managed service that gives you access to Google’s advanced load-balancing technologies, its worldwide network, and GCP’s range of data and machine-learning managed services.

The specialization comprises of 4 courses that are delivered as a combination of video lectures, demonstrations and hands-on lab exercises. The courses are as follows:

1. Google Cloud Platform Fundamentals: Core Infrastructure – This course introduces participants to the four main kinds of services offered by GCP. It covers Google App Engine, Google Compute Engine, Google Kubernetes Engine, Google Cloud Storage, Google Cloud SQL, BigQuery, Google Virtual Network, various resource and policy management tools etc.
2. Architecting with Google Kubernetes Engine: Foundations – This course covers the foundations of architecting with GKE by reviewing the layout and principles of GCP, followed by an introduction to creating and managing software containers and an introduction to the architecture of Kubernetes.
3. Architecting with Google Kubernetes Engine: Workloads – In this course students learn about performing Kubernetes operations, creating and managing deployments, the tools of GKE networking, and how to give Kubernetes workloads persistent storage.
4. Architecting with Google Kubernetes Engine: Production – In this course participants learn about Kubernetes and Google Kubernetes Engine (GKE) security, logging and monitoring, and using GCP managed storage and database services from within GKE.

This is a beginner level specialization, and requires participants to have an IT background, including working at the Linux command line, and working with networks and Web servers.

Key Highlights

• Discover the advantages of Google Kubernetes Engine compared to building your own container-management infrastructure
• Learn how to configure your Google Kubernetes Engine environment; build, schedule, load-balance, and monitor workloads; manage access control and security; and give your applications persistent storage
• Understand how to deploy practical solutions including security and access management, resource management, and resource monitoring
• Learn the components of a Kubernetes cluster and how they work together and how to deploy a Kubernetes cluster
• Learn about the different types of Kubernetes storage abstractions

Duration : 5 weeks, 16 hours per week
Rating : 4.7
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10. Data Engineering, Big Data, and Machine Learning on GCP Specialization by Google Cloud (Coursera)

The amount of data created each year is growing faster than ever before. The skills to build systems that can handle and utilize large amounts of data are therefore hugely desired by the organizations. Google Cloud provides the infrastructure to build these systems. This specialization designed by experts at Google helps participants launch their career in Data Engineering and makes it easy to become a google certified data engineer. It provides a hands-on introduction to designing and building data pipelines on Google Cloud Platform.

Through the series of lectures presented by Google Cloud practitioners, demos and practical exercises, participants are taught how to design data processing systems, build end-to-end data pipelines, analyze data and derive insights. The specialization covers structured, unstructured, and streaming data.

There are 5 courses in this specialization that cover the following:

1. Google Cloud Platform Big Data and Machine Learning Fundamentals – This course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP) and dives deeper into the data processing capabilities. They learn to use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to GCP, employ BigQuery and Cloud Datalab to carry out interactive data analysis, train and use a neural network using TensorFlow, choose between Cloud SQL, BigTable and Datastore.
2. Modernizing Data Lakes and Data Warehouses with GCP – The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud Platform in technical detail.
3. Building Batch Data Pipelines on GCP – Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud Platform for data transformation including BigQuery, executing Spark on Cloud Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Cloud Dataflow.
4. Building Resilient Streaming Analytics Systems on GCP – This course covers how to build streaming data pipelines on Google Cloud Platform. Cloud Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Cloud Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis.
5. Smart Analytics, Machine Learning, and AI on GCP – This course covers several ways machine learning can be included in data pipelines on Google Cloud Platform depending on the level of customization required. It also discusses AutoML, AI Platform Notebooks, BigQuery Machine Learning and how to productionalize machine learning solutions using Kubeflow.

The hands-on labs use an iterative platform called QwikLabs. These help learners gain practical hands-on experience with the concepts explained throughout the modules.

Being able to use cloud technologies is becoming a requirement for any kind of data focused role. So, this specialization is perfectly suitable for existing data scientists, data engineers, data analysts, machine learning engineers or anybody looking for a career change into the world of data.

Key Highlights

• Learn to process big data at scale for analytics and machine learning
• Design and build data pipelines on Google Cloud Platform
• Lift and shift your existing Hadoop workloads to the Cloud using Cloud Dataproc
• Learn the fundamentals of building new machine learning models
• Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
• Manage your data Pipelines with Data Fusion and Cloud Composer
• Derive business insights from extremely large datasets using Google BigQuery
• Learn how to use pre-built ML APIs on unstructured data and build different kinds of ML models using BigQuery ML
• Enable instant insights from streaming data
• Get hands-on experience with data lakes and warehouses on Google Cloud Platform using QwikLabs

Duration : 5 weeks, 10 hours per week
Rating : 4.6
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11. Developing APIs with Google Cloud’s Apigee API Platform Specialization by Google Cloud (Coursera)

Apigee is a tool that can manage the API gateway and make it easier to produce and deploy modern, developer-friendly apps. This Coursera Apigee specialization teaches participants how to develop APIs with Google Cloud’s Apigee API platform. It introduces them to the many unique capabilities of the Google Apigee and how to apply them to APIs to properly implement and secure them.

The specialization is delivered as a combination of video lectures, demos, hands-on labs and extra supplemental material, that together help students to learn how to design, build, and deploy their API solution using services on the Google Apigee Platform. A key highlight of this Apigee specialization is that it allows students to spin their own free environment and develop their first set of APIs from scratch as the instructor walks them through a specific real world scenario.

The three courses in the specialization are as follows:

1. API Design and Fundamentals of Google Cloud’s Apigee API Platform – This course gives an introductory look at the Apigee API Platform and API Design in general. It also discusses Apigee Edge UI and how to navigate through it, industry best practices and Apigee Technology Stack to ensure a full understanding of all the platform components.
2. API Development on Google Cloud’s Apigee API Platform – This course gives an in depth overview of API development on the Apigee API Platform. It covers various tools and out of the box policies available within Apigee Edge. Also discusses target servers, error handling and logging, shared flows, flow hooks and extensions, mediation, caching and Node.js integration etc.
3. API Security on Google Cloud’s Apigee API Platform – This course covers the topics related to API security including API keys, traffic management policies, as well as an overview of the capabilities of Apigee Sense. It teaches how to properly secure your APIs by covering topics such as the types of OAuth, TLS, and SAML to name a few.

This specialization is intermediate level and requires basic understanding of APIs and software development. It is recommended for Engineers, Architects and Developers who are responsible for the design, implementation and management of APIs and API Products using Apigee API Platform.

Key Highlights

• Learn to Design, Develop, and Secure APIs on Google Cloud’s Apigee API Platform
• Explore Apigee Technology Stack to ensure a full understanding of all the platform components
• Learn to create your own OpenAPI specification in Apigee Edge
• Learn the industry best practices for API design and implementation
• Gain a high level understanding of API security and why it’s important
• Learn how to integrate OAuth and grants to your existing API proxies
• Hands-on labs that set you up to the real world work

Duration : 1 month, 13 hours a week
Rating : 4.6
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12. Getting Started with Google Kubernetes Engine by Google Cloud (Coursera)

This course by Google Cloud is an abbreviated overview of Kubernetes that teaches the fundamental concepts of Kubernetes and Google Kubernetes Engine. It equips participants to containerize workloads in Docker containers, deploy them to Kubernetes clusters provided by Google Kubernetes Engine, and scale those workloads to handle increased traffic.

There are 5 modules in this GCP course that deliver the following skills:

• Understanding of container basics
• How to Containerize an existing application
• Understanding of Kubernetes concepts and principles
• Deploying applications to Kubernetes using the CLI
• Setting up a continuous delivery pipeline using Jenkins
• Locating more documentation and training

This course is suitable for learners with basic proficiency of command-line tools and Linux, and some knowledge about Web server technologies such as Nginx. Systems Operations experience is also desirable.

By the end of this course, you will have gained practical skills required to work on Google Cloud Platform, Continuous Delivery, Kubernetes and Jenkins software.

Key Highlights

• Acquaint yourself with containers, Docker, and the Google Container Registry
• Learn to deploy an application with microservices in a Kubernetes cluster
• Learn to create and manage Kubernetes deployments
• Learn to Build a continuous delivery pipeline

Duration : 1 week, 10-12 hours per week
Rating : 4.5
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13. Site Reliability Engineering: Measuring and Managing Reliability by Google Cloud (Coursera)

Site Reliability Engineering (SRE) is a discipline founded at Google that prescribes certain methods and principles for building and running reliable systems. It helps to reduce the operational burden of systems, makes them more agile, and thus capable to run reliable services for users and customers.

This Certificate program by Google aims to help participants get started with SRE. The experts at Google with years of collective SRE experience have created this program to make it easy for developers to learn the basics of SRE, adopt SRE and implement these principles so that they can design and manage complex systems that meet their reliability targets.

This SRE course teaches the theory of Service Level Objectives (SLOs), Service Level Indicators (SLIs), the components of a meaningful SLI and how to use SLIs to quantify reliability and Error Budgets to drive business decisions around engineering for greater reliability. It also walks the participants through the process of developing SLIs and SLOs for an example service.

The course is structured as a set of 7 modules spread over 4 weeks. It starts with the basics, including how it came to be part of Google engineering, and what kind of tools SREs use to make decisions. It talks about the concepts underpinning SRE, CRE, and SLOs, common monitoring practices, pros and cons of different measurement strategies, and specific recommendations on how to choose your own metrics. Then it dives into four step process for developing SLOs and SLIs for a user journey using a case study of a mobile game company. Then it goes to discuss a method for performing risk analysis and see how to incorporate those findings into your long-term reliability goals. Additionally, it covers documenting SLOs and assigning responsibilities to ensure setting up a sustainable SRE practice.

This is an advanced level course, but has flexible timelines so participants can learn at their own pace and find insight. It is suitable for both new and experienced site reliability engineers.

Key Highlights

• Learn how to make systems reliable
• Understand SLIs, SLOs and SLAs
• Learn to quantify risks to and consequences of SLOs
• Understand how to describe and measure the desired reliability of a service
• Learn about error budgets and how to use them
• Learn how to measure against your metrics and assess whether they’re realistic

Duration : 4 weeks, 4-5 hours per week
Rating : 4.4
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14. Google Certified Associate Cloud Engineer Certification (Udemy)

This Udemy google cloud course is one of the best online courses available to train for Google Cloud Associate Cloud Engineer (ACE) certification. It is a comprehensive course that gives an efficient introduction to everything that GCP has to offer, thereby opening the doors to Google Cloud, even for participants with no prior GCP experience. It efficiently teaches the skills required by the ACE certification—namely deploying applications, monitoring operations, and managing enterprise solutions.

This is a very interactive and hands-on course. Students learn to set-up and configure Google Cloud environment including accounts using the best practices. It covers securing, scaling, networking and whatever one needs to know about Google Kubernetes Engine. It is basically a combination of 3 courses – Introduction to Google Cloud Platform, Google Certified Associate Cloud Engineer and Kubernetes Deep Dive by Nigel Poulton. Thus this course helps students to both pass the Google certification exam and also learn how to use Google Cloud in real life.

The course has been created by Ryan Kroonenburg and his team members at ‘A Cloud Guru’. Ryan is an authority when it comes to Cloud and knows it inside and out. He has been working in Cloud space since its very inception. Though Ryan does not directly teach the course, the quality is guaranteed, both in terms of content and delivery. The course material is instructed by Mattias Andersson who is a subject matter expert on Google Cloud. The Google Kubernetes Engine part of the course is taught by Nigel Poulton, who is a popular figure in the container technology industry, best known for his mind-blowing Kubernetes and Docker Video Training Courses and Books. He is a highly sought-after Public Speaker at tech conferences around the world.

This course is perfect for developers and architects who want to design and build applications for Google Cloud, and system administrators who wish to learn how to configure and manage Google Cloud systems and demonstrate those abilities through certification.

Many students have used this course not only for passing Google Cloud Associate Cloud Engineer (ACE) certification but also Google Cloud Professional Cloud Architect (PCA) certification exam. It gives students the solid foundation of GCP capability that they need to build toward the Professional Cloud Architect (PCA) certification.

Key Highlights

• Pass the GCP Associate Cloud Engineer certification exam
• Use GCP services like Cloud Storage, Compute Engine, and Kubernetes Engine in your everyday work
• Set up a Google Cloud environment, including billing accounts, projects, tools, access, and security
• Get familiar with using the Google Cloud through both the console and the command-line
• Plan, configure, implement, deploy, monitor, and manage solutions in the Google Cloud
• Build a strong foundation for other GCP certification exams

Duration : 14.5 hours on-demand video
Rating : 4.4
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15. GCP: Complete Google Data Engineer and Cloud Architect Guide (Udemy)

This Udemy GCP Course is a comprehensive guide to Google Cloud Platform. It is regarded as one of the best online course for learning the fundamentals of Google Cloud and its big data and machine learning technologies as well as to train for Google Cloud Professional Data Engineer and Cloud Architect certification exams.

This course teaches students everything that they need to know for leveraging the capabilities of GCP and building TensorFlow Machine Learning models on the google cloud. It covers the whole range of Google Cloud’s Big Data technologies like, DataFlow, DataProc, BigTable and BigQuery. Along the way, students learn the compute options available like, App Engine, Compute Engine, Containers and Kubernetes. The Machine Learning and TensorFlow are covered in a lot of detail and from first principles approach.

The course content includes the following modules:

• Compute and Storage – AppEngine, Container Engine (aka Kubernetes) and Compute Engine
• Big Data and Managed Hadoop – Dataproc, Dataflow, BigTable, BigQuery, Pub/Sub
• TensorFlow on the Cloud – What neural networks and deep learning really are, how neurons work and how neural networks are trained
• DevOps stuff – StackDriver logging, monitoring, cloud deployment manager
• Security – Identity and Access Management, Identity-Aware proxying, OAuth, API Keys, service accounts
• Networking – Virtual Private Clouds, shared VPCs, Load balancing at the network, transport and HTTP layer; VPN, Cloud Interconnect and CDN Interconnect
• Hadoop Foundations – A quick look at the open-source cousins (Hadoop, Spark, Pig, Hive and HBase)

The instructors of the course are very well qualified and have years of experience working in the tech industry. They have spent several years at Google so have first-hand experience with Google Cloud technologies. They have a neat way of teaching complicated tech courses in a funny, practical and engaging way.

The course has a rich content of 28 hours on-demand video, 25 articles and 48 downloadable resources. There are several practice tests also included. It is intended for architects and engineers looking to use the Google Cloud Platform in their organizations, or data scientists looking to deploy their TensorFlow models on the cloud.

Key Highlights

• Use big data technologies such as BigTable, Dataflow, Apache Beam and Pub/Sub
• Build deep learning models on the cloud using TensorFlow
• Deploy Managed Hadoop apps on the Google Cloud
• Make informed decisions about Containers, VMs and AppEngine
• Pass the Google Cloud Professional Data Engineer Certification Exam

Duration : 28 hours on-demand video
Rating : 4.3
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