(GCP vs AWS vs Azure )War of the Giants.
Google Cloud Platform(GCP), Amazon Web Service(AWS), and Microsoft Azure are the Cloud service offered by the three big giants.
We won’t consider Alibaba Cloud Service which was launched in 2017 and its still has to go lots of major upgrade to match up with others.
Public cloud service accounts for 72% of the worldwide market share for IaaS (1) (Infrastructure as a Service) and PaaS (2)(Platform as a service). Public cloud Service also grows more then private cloud services.
The above Figure clearly states the fact Amazon owns 40 percent of Public Cloud Service Market.
While the others there has been steady increase in Share.
Meanwhile Azure has a strong hold in SaaS.
Amazon Web Services(IaaS)- Public Cloud. Prevents Interoperating (4) with your data center andenterprise-ready provider.
Comprehensive network of worldwide data center.
Difficult Cost-Structure hence third-party cost management tools is highly recommended.
Microsoft Azure- For Enterprise purpose. Hybrid Cloud (5)and Interoperatability(4).
Its success comes from the fact due to widespread popularity of Windows, Office, .NET support and other software.
Support for open-source
Cons-
Documentation, technical support, training and breadth of the ISV partner ecosystem.
Google Cloud- For technical Expertise is profound, and its industry-leading tools in DL, AI and ML and data analytics are significant advantages.
Google Cloud also developed Kubernetes standard which is offered by AWS and Azure.
Offers scalibility and load balancing.
Google uses deep discounts and contracts with Organisation.
But overall it offers less service compared to others.
Less Global Data Center.
Not enterprise focused.
Compute
AWS
Elastic Compute Cloud (EC2)- flagship of Amazon.
Web services that provides security, realizable compute capacity in the cloud.
Support for both Windows and Linux, GPU instances, high-performance computing, auto-scaling and more.
Includes 750 hours per month free tier for twelve months.
Container Services- Support for Docker, Kubernetes and its own Fargate service that automates server and cluster management when using container.
Batch for batch computing jobs
Elastic Beanstalk for running and scaling Web Application.
Virtual private cloud options known as Lightsail.
Microsoft Computer
Virtual Machines- Support for Both Linux, Windows Server , SQL Server , Oracle, IBM and SAP.
It includes an extremely large catalog of available instances, including High-performance computing and GPU.
Includes 750 hours per month of Windows or Linux B1S virtual machines for a year.
Contains two container services: Azure Container Service is based on Kubernetes, and Container Services uses Docker Hub and Azure Container Registry for management.
It has a Batch service, and Cloud Services for scalable Web applications is similar to AWS Elastic Beanstalk.
Service Fabric that is specifically designed for microservice architecture applications.
Google Compute:
Compute Engine-boasts both custom and predefined machine types, per-second billing, Linux and Windows support,
Carbon-neutral infrastructure that uses half the energy of typical data centers.
offers a free tier that includes one f1-micro instance per month for up to 12 months.
Focuses on Kubernetes for deploying containers.
Includes support for Micro-service
AWS vs. Azure vs. Google: Storage
AWS Storage:
SSS to EFS: AWS
Simple Storage Service (S3) for object storage
Object storage is a data storage strategy that sections data into distinct units, or objects, which are stored in an isolated storehouse along with all relevant metadata and a custom identifier.
Elastic Block Storage (EBS) for persistent block storage for use with EC2.
Persistent storage is a concept in cloud-hosted data persistence where cloud services emulate the behaviour of a traditional block device, such as a physical hard drive. It is a form of network-attached storage (NAS). Storage in such is organised as blocks.
Disaster Recovery
Elastic File System (EFS) for file storage.
Storage Gateway, which enables a hybrid storage environment, Snowball, which is a physical hardware device that organizations can use to transfer petabytes of data.
Database and archiving On the database side, Amazon has a SQL-compatible database called Aurora, Relational Database Service (RDS), DynamoDB NoSQL database, ElastiCache in-memory data store, Redshift data warehouse, Neptune graph database and a Database Migration Service.
Amazon offers Glacier, which is designed for long retrieval time storage at very low rate.
Archive process selects data from a source (one or more tables in a database) and copies that data to a destination (an archive file).
Azure Storage:
Site Recovery- For automatic and Protection and Disaster Recovery.
Storage Services: Blob Storage for REST-based object storage of unstructured data.
Hybrid Storage- Storage Gateway.
Queue Storage for large-volume workloads, File Storage and Disk Storage.
It also has a Data Lake Store, which is useful for big data applications.
Extensive Database:support three SQL-based options: SQL Database, Database for MySQL and Database for PostgreSQL.
It also has a Data Warehouse service, as well as Cosmos DB and Table Storage for NoSQL.
Redis Cache is its in-memory service and the Server Stretch Database is its hybrid storage service designed specifically for organizations that use Microsoft SQL Server in their own data centers.
-does offer an actual Backup service, as well as Site Recovery service and Archive Storage.
Google Storage:
Archive Storage- Nearline, Coldline
Unified Storage and more: Cloud Storage is unified object storage service, and it also has a Persistent Disk option. It offers a Transfer Appliance and Online Service Transfer
SQL and NoSQL — SQL-based Cloud SQL and a relational database called Cloud Spanner that is designed for mission-critical workloads.
It also has two NoSQL options: Cloud Bigtable and Cloud Datastore.
It does not support backup and archive services .
AWS vs. Azure vs. Google: Key Cloud Tools
AWS Key Tools:
Amazon ElastiCache is a fully managed in-memory data store and cache service by Amazon Web Services. The service improves the performance of web applications by retrieving information from managed in-memory caches, instead of relying entirely on slower disk-based databases.
Pagemaker to Serverless: SageMaker service for training and deploying machine learning models,
the Lex conversational interface that powers its Alexa services, its Greengrass IoT messaging service and the Lambda serverless computing service.
Caching- ElastiCache
AI and ML: offers DeepLens, an AI powered camera for OCR and image and object recognition.
Azure Key Tools:
Caching- RedisCache.
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache and message broker.
Cognitive Services that include a Bing Web Search API, Text Analytics API, Face API, Computer Vision API and Custom Vision Service.
For IoT, it has several management and analytics services, and its serverless computing service is known as Functions.
Key Terms
Serverless computing is a cloud computing execution model in which the cloud provider runs the server, and dynamically manages the allocation of machine resources. … Serverless computing can simplify the process of deploying code into production.
Supporting MSFT Software -Azure Backup links Windows Server Backup in Windows Server 2012 R2 and Windows Server 2016. Visual Studio Team Services hosts Visual Studio projects on Azure.
Google Key Tools:
Caching- Cloud CDN
-Does not offer database migration and mangement of Data Warehouse.
-Ever since the introduction of Tensorflow, Google cloud Platforms are big area of Focus for AI, Machine Learning
-Offers support for NLP.
Pricing Models
The basic instance which include 2 virtual CPUs and 8 GB of RAM will cost around
AWS
$69 per month.
Azure
$70/month
GCP
$52/month
Largest Instance
AWS
Largest instance which include 3.84TB of RAM and 128 vCPUs cost around $3.97/hour.
Azure
include 3.89TB of RAM and 128 vCPUs cost $6.79/hour.
GCP
instance that includes 3.75 TB of RAM and 160 vCPUs cost around $5.32 /hour
Winner -Amazon Web Service
Early establishment, great number of region and availability, larger market share and more number of services makes the Amazon Web Service clear winner….
In terms of Pricing Model and Growth rate, Google takes an edge.
Azure is however good regarding the integration with open-sources and MS Tools.
Key Terms
First
PaaS-Platform as a Service- It is a cloud delivery model for application composed of services managed by third party. It is an abstract and integrated cloud-based computing environment that supports the development, running and management of applications. It is a way to rent hardware, operating system, storage and network capacity over the Internet.
Primary objective of PaaS is that abstraction i.e Developers does not have to be concerned about lower-level details of the environment. It is the outgrowth of Software as a Model, a model via which software is made available to Customers over the Internet.
User does not have to focus on Operating System, File System , User Authentication, Utilities , Logs and Database administration. It is easily scalable hence client does not have to worry about problem of sharing of the underlying infrastructure between users, and that results in lower cost.
Second
IaaS- It means delivering computing infrastucture as on-demand service to organizations via virtualization technology that help organizations build.IaaS provides Server, Storage, Network and Operating System.
Example -AWS, Azure
Features of IaaS
Resources are distributed as a Service.
Allows dynamic scaling (1..10…100….)
Multi-tenancyDisadvantage of PaaS model is you can only control what’s built on the platform — if there is an outage or issue regarding hardware or Operating System that platform is built on, it will take out the software with them.
A great example is Salesforce.
Fourth
Data interoperability addresses the ability of system and services that create, exchange and consume data to have clear, shared expectations for the contents context and meaning of data.
Fifth
Hybrid cloud is a cloud computing environment that uses a mix of on-premises, private cloud and third-party, public cloud services with orchestration between platform
