Google Cloud Platform – One Of The Largest Cloud Service For Business
Google Cloud Platform (GCP) is ranked third in the ranking of the largest cloud platforms, second only to such industry titans as Amazon Web Services and Microsoft Azure.
Content:
1. Google Cloud Platform Infrastructure Overview
2. The history of the creation and development of the GCP project
3. Key services of Google Cloud Platform
4. For what purposes and areas is the Google Cloud Platform suitable?
5. Conclusion. One of the largest business platforms
At the end of 2021, GCP still holds 5-6% of the market share, but its revenue for the current year grew by 46%, amounting to $ 4.047 billion. This growth has been driven by a significant expansion of the services offered in the data processing and analytics sectors. The company is gradually closing its significant gap with the market leaders, including by providing customers with competitive rates for its services.
We continue the series of articles on cloud platforms and decided to devote our next article to Google Cloud Platform. In this article, you will learn about the key capabilities and advantages of this platform, the history of its emergence and development, and also get acquainted with the brief characteristics of its main services.
Google Cloud Platform Infrastructure Overview
The GCP suite of cloud services is powered by Google's infrastructure, which is also used by the company to run its B2C services like Google Search, YouTube, and more. This makes it available in more than 200 countries and regions of the world, ensuring the maximum speed and performance of services.
Google Cloud Platform's global infrastructure is spread across 29 cloud regions, 88 zones, and 146 locations in the Americas, Europe, the Middle East, Africa, Oceania, and Asia Pacific.
The set of products of the platform includes over 100 services, including ready-made infrastructure, management tools and a number of modular-type cloud services: for computing, analyzing and storing data, as well as machine learning (ML). Google's infrastructure and resources support all popular service delivery models that are used in the development and deployment of software as a service applications or other projects. In particular, it is PaaS (platform as a service), IaaS (infrastructure as a service), serverless computing, etc.
The history of the creation and development of the GCP project
2008-2017
The first information about Google Cloud Platform services dates back to 2008, when the company announced the development of the flagship App Engine service. It was the first cloud-based software introduced by Google. In 2010, Google Cloud Storage was launched, a file hosting service that is accessed via REST. In addition, in the same year, the developers announced Google BigQuery and the Prediction API.
In 2011, the company released the Google Cloud SQL announcement, and a year later launched the Google Compute Engine. In 2013, an important update of the Cloud Storage security system appeared: the service began to automatically encrypt all objects of stored data and metadata using the AES-128 encryption standard and additionally with a set of master keys.
In 2014, Google announced the open source container manager Kubernetes, in 2015 the corporation introduced a whole set of cloud services: Google Cloud Monitoring, Google Cloud Pub / Sub, Google Cloud DNS, Google Dataflow. All of them have also become available as part of the Google Analytics platform. In 2015, she launched a cloud backup service for business called Nearline Storage, which allows you to access the requested files in a few seconds after sending the request. A year later, the GCP line was supplemented with the Stackdriver service (later changed its name to Operations: Cloud Monitoring & Logging), designed to manage cloud computing systems.
In February 2017, the beta version of Cloud Spanner, a cluster relational database management system (RDBMS), was released. Shortly thereafter, Kaggle, the world's largest community of data science and machine learning enthusiasts, joined the work on Google Cloud Platform services.
Later in the same 2017, a beta version of the Google Cloud IoT Core system was launched, designed to connect, transfer, manage and process data from millions of globally distributed Internet of Things (IoT) devices. The official release of this service took place a year later. In November 2017, Google Kubernetes Engine was certified by the Cloud Native Computing Foundation (CNCF).
2018-2021
In 2018, the Google Cloud Storage product line expanded with a beta version of Google TPU (Tensor Processor Unit), a tensor processor for the TensorFlow machine learning library. At the same time, the well-known research and consulting company Gartner awarded Google the title of leader in the annual Service Magic Quadrant. Also in 2018, the corporation launched a beta version of Memorystore, an in-memory service for reducing data processing latency in Redis and Memcached systems.
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2019 was marked by the addition of a new service to the GCP line - Cloud Run, designed to develop and deploy scalable container applications based on a flexible serverless platform. At the same time, Google Anthos was announced, a cloud-based application development platform that allows you to use Google Cloud tools in a user-friendly environment (be it a GC, another cloud platform, or a local resource).
In 2020, Google's cloud product line has expanded with two specialized AI services called Healthcare Natural Language API and AutoML Entity Extraction for Healthcare. The first is used to extract information from unstructured medical documents or other texts. The second allows developing ML-models for extracting named entities from medical documents, as well as keeping a text record of the communication between doctors and patients. Both services are able to integrate into the software of medical institutions through the API.
Another product with similar options is the Document AI AI service for automatically extracting data from forms and documents using machine learning algorithms. The company developed it in the same 2020, at the same time it announced that Google Cloud would become a possible candidate for the development of blocks for the EOS network and the EOS.IO protocol.
In 2021, she released the announcement of the Google Kubernetes Engine Autopilot service, designed to reduce the cost of managing and optimizing clusters. In addition, Google introduced the 4th generation of Tensor Processing Units (TPU) AI chips with higher processing power - more than 1 exaflop per module.
Key Google Cloud Platform Services
- App Engine. The first and flagship service in the GCP line, launched in 2008. Provided on a PaaS (Platform as a Service) model, designed for the development and deployment of web services and mobile applications. Has a simple and functional management system, scalable hosting and a large selection of APIs. Supports applications in many popular programming languages, including Java, PHP, Node.js, Python, C #, .Net, Ruby, Go, and more. Basic App Engine resources are available for free for review; on a paid plan, users pay only for the amount of service resources they have used (pay-as-you-go).
- BigQuery. The most popular product in the Google BigData line. It is a cloud-based, serverless service running in IaaS (infrastructure as a service) format. Designed for storing and analyzing large amounts of data, allows you to manage them (create, delete, import), create queries, provide access to data to third parties or groups, as well as integrate data with various software via Google Apps Script or REST API. There is also a function for creating and running machine learning models through SQL queries. All BigQuery users get 10 GB of cloud storage space for free and up to 1 TB of queries per month.
- Compute Engine (GCE). This service operates in IaaS (infrastructure as a service) format, allowing you to create and run virtual machines (VM) based on Google infrastructure from standard or custom images. The GCE is accessed through a command line interface, RESTful interface, or developer console. Other features of the service include point virtual machines (a budget option to reduce costs), encryption of processed data, and resource optimization using autorecommendations. Each user gets one general purpose VM (e2-micro instance) free of charge.
- Kubernetes Engine (GKE). Service for working with containerized applications on the Kubernetes platform, including their deployment, scaling and coordination in an automatic mode. Allows you to create clusters in one click and scale them up to 15,000 nodes. There are a number of security options, including data encryption and container scanning to find vulnerabilities. Supports common containerization technologies (Docker, rkt) and hardware virtualization.
- Cloud Storage. Cloud RESTful hosting of unstructured data, working on the principle of IaaS (infrastructure as a service). Combines the performance and scalability of the standard Google Cloud with a range of additional capabilities. CS allows you to save objects up to 5 TB in size in buckets and assign a unique key to each of them. You can also flexibly manage data by automatically sending it to cheaper storage, and also optimize and reduce unnecessary data.
- Datastore. Scalable NoSQL database for web and mobile applications. Supports REST API, indexing, SQL-like queries and ACID transactions. The functionality of the service makes it possible to automatically manage sharding and replication, it is optimal for processing data sets of compact size - groups of objects. The Datastore database is developed on the basis of Google BigTable and Megastore technologies.
For what purposes and areas is the Google Cloud Platform suitable?
- Modernization of infrastructure. The cloud platform allows you to run VMware, SAP, Oracle and Windows software directly from Google Cloud.
- Creation and maintenance of databases. Transfer and manage enterprise data using a line of functional, secure, reliable and fast data services.
- Application modernization. Updating and supplementing applications, developing new services based on containers, Kubernetes platform and other cloud functions.
- Smart analytics. The built-in serverless analytics platform provides a rich set of capabilities - from quick insights to deep complex analysis.
- AI&ML. GCP contains more than a dozen services in the field of artificial intelligence (AI) and machine learning (ML): for building and training ML-models, developing dialog interfaces, analyzing texts, images and videos, translations, etc.
- Safety. The platform provides a range of solutions in the field of security analytics, protection of web applications and APIs (WAAP), creation of full cycle cybersecurity systems for cloud and on-premises products.
- Business applications. Users can create different types of business applications without programming skills, flexibly automate processes and extend the capabilities of their external products using Google resources, integrating them via API.
- Productivity and collaboration. GCP-integrated Google Workspace tools help make collaboration easier and faster, making it more efficient, while direct access to Gmail, Docs, Drive, and Meet services will increase the productivity of teams, teams and businesses.
- Retail. Google Cloud provides a range of retail solutions including e-commerce, data warehousing, SAP, API management, merchandising, marketing, customer service, and more.
- Financial services. GCP users have access to a range of specialized financial services, including online banking, data storage and management, quantitative computing, AI-enabled contact center, risk modeling, reporting, and more.
- Healthcare. GCP tools for medicine and healthcare cover solutions for telemedicine, remote admission and patient care, data processing, analytics and insights, applications in the "Health and wellness" category, service visualization, etc.
- Media and entertainment. The platform's functionality allows you to create various types of digital content, optimize workflows, launch new digital services and transform user experience.
In addition, the platform's services provide sets of ready-made solutions for state, municipal and public organizations, manufacturing and industry, game development and a number of other industries.
Conclusion. One of the largest business platforms
The Google Cloud Platform includes over 100 cloud services and services, divided into a number of categories: cloud computing, storage and databases, networks and communications, big data, artificial intelligence and machine learning, management tools, identity and security, internet of things, API platforms.
The most famous and popular among them are a service for hosting web services and App Engine mobile applications, a service for storing and analyzing BigQuery big data, a service for developing and running Compute Engine virtual machines, a service for working with Kubernetes Engine containers, hosting Cloud Storage data, as well as a database for web and mobile applications Datastore.
The GCP platform provides turnkey solutions for a number of goals, objectives and areas, including infrastructure and application modernization, analytics, collaboration, security, and also retail, finance, media, medicine, manufacturing, etc.
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