Google Cloud Platform (GCP) is Google’s answer to the hardware and software services that make up cloud computing. As the cloud computing market prospers, big tech companies are seizing the opportunity to power digital transformation with cloud platform offerings.
While fellow tech giants, Amazon (with Amazon Web Services — AWS) and Microsoft (with Azure), lead in market share of cloud products, Google is catching up to become the fastest-growing cloud platform. Gartner projects that the cloud services market will see 3 times the growth of overall IT services through 2022. Tech leads looking to expand their companies’ cloud investments or IT professionals wanting to keep their skills competitive should have familiarity with the major cloud players, which is why I’ve created a Google Cloud Platform fundamentals course. In this article, we’ll take an introductory look at Google Cloud Platform and what I cover in my course.
What is Google Cloud Platform?
Google Cloud Platform is a public cloud service built on the same infrastructure Google uses for its products including Search, YouTube, and Gmail. Google Cloud offers cloud computing essentials that help companies scale their IT infrastructure beyond onsite server solutions, which can become cost prohibitive as a business grows. These cloud essentials include core infrastructure, platform, databases, analytics, machine learning, IoT, and a set of enterprise services. Customers can fine-tune their Google Cloud Platform use by consuming available APIs or using its core infrastructure and platform to build highly customized applications.
Google Cloud vs. AWS and Azure
Google Cloud is the third-largest public cloud Infrastructure as a Service cloud provider, with AWS and Microsoft Azure leading the top three. While each cloud platform has its own set of unique advantages and disadvantages, they fundamentally offer many of the same services. Where Google Cloud Platform shines is with its container, machine learning, and data offerings. Google Kubernetes Engine is one of the key building blocks of Google Cloud Platform, which is the industry’s first managed container orchestration service in the public cloud. BigQuery is a highly scalable data warehouse in the cloud. The Google AI Platform offers APIs and “out of the box” tools for companies to scale AI uses.
How do these strengths compare to AWS and Azure? AWS is, arguably, the leader of cloud computing with a formidable 165+ product offerings and a strong global infrastructure. A survey from Accenture named AWS the most developer-friendly and future-looking platform. In the same survey, Microsoft’s Azure cloud platform received top marks for its accuracy, content readability, and technical support.
Many AWS and Azure customers add Google Cloud to their cloud architecture for the mature data and analytics offerings. Google Cloud originally positioned itself as the solution for small and medium-sized businesses, a segment it still sees popularity with. But with its focus on hybrid and multi-cloud, Google Cloud Platform is fast becoming the choice of enterprises.
Google Cloud as part of a multi-cloud solution
Before implementing a cloud solution, companies should define a cloud strategy and migration plan. Begin by creating an inventory of all the applications running in the organization, and assess each for its cloud-readiness. Through this process, many tech teams I work with recognize that a single cloud provider isn’t always the best choice. One vendor may pair better with existing storage applications, while another may be best for spinning up virtual machines, while an on-site server can best accommodate the scale of a smaller application.
Companies of all sizes are adopting hybrid (a combination of on-premise and cloud infrastructure) and multi-cloud solutions as they stray from on-premise options. One way Google secures itself as part of an organization’s cloud architecture is by using Kubernetes as the platform to bridge the gap between on-premises infrastructure and cloud infrastructure. Built on an open-source foundation, Anthos extends Kubernetes, Istio, and Knative to address the challenges of multi-cloud deployments. With Anthos, enterprises can manage traditional and modern workloads running within their data center, Google Cloud, and even other cloud platforms.
Google Cloud for AI and data science
With initiatives like TensorFlow, an open-source machine learning library, Google has become known for its expertise in machine learning and artificial intelligence. That distinction also carries to Google Cloud, which offers core components to manage the end-to-end workflow involved in data science and AI applications. During a 2019 earnings call, Google CEO Sundar Pichai noted that Google Cloud’s machine learning offerings are what help the company drive differentiation in a very competitive market.
Services such as Cloud Pub/Sub, Cloud Dataflow, and Cloud Dataproc handle data ingestion, data preparation, and data processing. Google AI Platform includes both high-level AI building blocks that expose APIs for vision, translation, and speech-based AI services. GCP’s Cloud AutoML platform makes it easy for customers to use custom datasets and build sophisticated machine learning models without writing complex code. Google also made Tensor Processing Unit (TPUs) available on its platform so that customers can accelerate training and inferencing of deep learning neural networks. BigQuery has in-built machine learning algorithms that can be used to perform predictive analytics.
Getting Google Cloud Platform Certified
If your organization is planning on adding Google Cloud, Google offers two levels of Cloud certifications, associate and professional. The associate-level certification focuses on job tasks related to Google Cloud technology. The professional certifications are role-based and assess advanced Google Cloud Platform design and implementation skills through hands-on experience. There are multiple specializations within the professional-level certification track including networking, data, and security proficiencies.
I recommend developers and IT professionals who want to learn the basics of Google Cloud Platform start with the associate-level certification and move to the professional level once they identify which specializations are needed by their employers or more aligned with their future career goals.
My course Google Cloud Platform (GCP) Fundamentals for Beginners offers a first step into the world of Google Cloud with case studies and an outline of the platform’s essential building blocks. For those already familiar with Google Cloud, the Google Certified Associate Cloud Engineer 2019 Prep Course will prepare you to pass the Google Associate Cloud Engineer exam and covers best practices for working with the services outlined in this article, from Kubernetes Engine to BigQuery to CloudFunctions and more.
About the author:
Janakiram MSV is a practicing software architect, analyst, and advisor with a focus on emerging infrastructure technologies. He provides strategic advisory to technology platform companies, startups, ISVs, and enterprises. Janakiram is recognized as a Google Developer Expert for Cloud and IoT, Microsoft Most Valuable Professional, Microsoft Regional Director, IBM Champion, Intel Software Innovator, and an Ambassador for the Cloud Native Computing Foundation. Janakiram regularly writes for Forbes Technology and The New Stack.
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