What are unique distinctions and similarities between AWS, Azure and Google Cloud services? For each AWS service, what is the equivalent Azure and Google Cloud service? For each Azure service, what is the corresponding Google Service? AWS Services vs Azure vs Google Services? Side by side comparison between AWS, Google Cloud and Azure Service?
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1
Category: Marketplace
Easy-to-deploy and automatically configured third-party applications, including single virtual machine or multiple virtual machine solutions.
References:
[AWS]:AWS Marketplace
[Azure]:Azure Marketplace
[Google]:Google Cloud Marketplace
Tags: #AWSMarketplace, #AzureMarketPlace, #GoogleMarketplace
Differences: They are both digital catalog with thousands of software listings from independent software vendors that make it easy to find, test, buy, and deploy software that runs on their respective cloud platform.
2
Category: AI and machine learning
A cloud service to train, deploy, automate, and manage machine learning models.
References:
[AWS]:AWS SageMaker(build, train and deploy machine learning models), AWS DeepComposer (ML enabled musical keyboard), Amazon Fraud Detector (Detect more online fraud faster), Amazon CodeGuru (Automate code reviews and identify expensive lines of code), Contact Lens for Amazon Connect (Contact center analytics powered by ML), Amazon Kendra (Reinvent enterprise search with ML), Amazon Augmented AI (Easily implement human review of ML predictions), Amazon SageMaker Studio (The first visual IDE for machine learning), Amazon SageMaker Notebooks (Quickly start and share ML notebooks), Amazon SageMaker Experiments (Organize, track, and evaluate ML experiments), Amazon SageMaker Debugger (Analyze and debug ML models in real time), Amazon SageMaker Autopilot (Automatically create high quality ML models), Amazon SageMaker Model Monitor (Continuously monitor ML models)
[Azure]:Azure Machine Learning
[Google]:Google Cloud TensorFlow
Tags: #AI, #CloudAI, #SageMaker, #AzureMachineLearning, #TensorFlow
Differences: According to the StackShare community, Azure Machine Learning has a broader approval, being mentioned in 12 company stacks & 8 developers stacks; compared to Amazon Machine Learning, which is listed in 8 company stacks and 9 developer stacks.
3
Category: AI and machine learning
Build and connect intelligent bots that interact with your users using text/SMS, Skype, Teams, Slack, Office 365 mail, Twitter, and other popular services.
References:
[AWS]:Alexa Skills Kit (enables a developer to build skills, also called conversational applications, on the Amazon Alexa artificial intelligence assistant.)
[Azure]:Microsoft Bot Framework (building enterprise-grade conversational AI experiences.)
[Google]:Google Assistant Actions ( developer platform that lets you create software to extend the functionality of the Google Assistant, Google’s virtual personal assistant,)
Tags: #AlexaSkillsKit, #MicrosoftBotFramework, #GoogleAssistant
Differences: One major advantage Google gets over Alexa is that Google Assistant is available to almost all Android devices.
4
Category: AI and machine learning
Description:API capable of converting speech to text, understanding intent, and converting text back to speech for natural responsiveness.
References:
[AWS]:Amazon Lex (building conversational interfaces into any application using voice and text.)
[Azure]:Azure Speech Services(unification of speech-to-text, text-to-speech, and speech translation into a single Azure subscription)
[Google]:Google APi.ai, AI Hub (Hosted repo of plug-and-play AI component), AI building blocks(for developers to add sight, language, conversation, and structured data to their applications.), AI Platform(code-based data science development environment, lets ML developers and data scientists quickly take projects from ideation to deployment.), DialogFlow (Google-owned developer of human–computer interaction technologies based on natural language conversations. ), TensorFlow(Open Source Machine Learning platform)
Tags: #AmazonLex, #CogintiveServices, #AzureSpeech, #Api.ai, #DialogFlow, #Tensorflow
Differences: api.ai provides us with such a platform which is easy to learn and comprehensive to develop conversation actions. It is a good example of the simplistic approach to solving complex man to machine communication problem using natural language processing in proximity to machine learning. Api.ai supports context based conversations now, which reduces the overhead of handling user context in session parameters. On the other hand in Lex this has to be handled in session. Also, api.ai can be used for both voice and text based conversations (assistant actions can be easily created using api.ai).
5
Category: AI and machine learning
Description:Computer Vision: Extract information from images to categorize and process visual data.
References:
[AWS]:Amazon Rekognition (based on the same proven, highly scalable, deep learning technology developed by Amazon’s computer vision scientists to analyze billions of images and videos daily. It requires no machine learning expertise to use.)
[Azure]:Cognitive Services(bring AI within reach of every developer—without requiring machine-learning expertise.)
[Google]:Google Vision (offers powerful pre-trained machine learning models through REST and RPC APIs.)
Tags: AmazonRekognition, #GoogleVision, #AzureSpeech
Differences: For now, only Google Cloud Vision supports batch processing. Videos are not natively supported by Google Cloud Vision or Amazon Rekognition. The Object Detection functionality of Google Cloud Vision and Amazon Rekognition is almost identical, both syntactically and semantically.
Differences:
Google Cloud Vision and Amazon Rekognition offer a broad spectrum of solutions, some of which are comparable in terms of functional details, quality, performance, and costs.
6
Category: Big data and analytics: Data warehouse
Description:Cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP) to quickly run complex queries across petabytes of data.
References:
[AWS]:AWS Redshift (scalable data warehouse that makes it simple and cost-effective to analyze all your data across your data warehouse and data lake.), Amazon Redshift Data Lake Export (Save query results in an open format),Amazon Redshift Federated Query(Run queries n line transactional data), Amazon Redshift RA3(Optimize costs with up to 3x better performance), AQUA: AQUA: Advanced Query Accelerator for Amazon Redshift (Power analytics with a new hardware-accelerated cache), UltraWarm for Amazon Elasticsearch Service(Store logs at ~1/10th the cost of existing storage tiers )
[Azure]:Azure Synapse formerly SQL Data Warehouse (limitless analytics service that brings together enterprise data warehousing and Big Data analytics.)
[Google]:BigQuery (RESTful web service that enables interactive analysis of massive datasets working in conjunction with Google Storage. )
Tags:#AWSRedshift, #GoogleBigQuery, #AzureSynapseAnalytics
Differences: Loading data, Managing resources (and hence pricing), Ecosystem. Ecosystem is where Redshift is clearly ahead of BigQuery. While BigQuery is an affordable, performant alternative to Redshift, they are considered to be more up and coming
7
Category: Big data and analytics: Data warehouse
Description: Apache Spark-based analytics platform. Managed Hadoop service. Data orchestration, ETL, Analytics and visualization
References:
[AWS]:EMR, Data Pipeline, Kinesis Stream, Kinesis Firehose, Glue, QuickSight, Athena, CloudSearch
[Azure]:Azure Databricks, Data Catalog Cortana Intelligence, HDInsight, Power BI, Azure Datafactory, Azure Search, Azure Data Lake Anlytics, Stream Analytics, Azure Machine Learning
[Google]:Cloud DataProc, Machine Learning, Cloud Datalab
Tags:#EMR, #DataPipeline, #Kinesis, #Cortana, AzureDatafactory, #AzureDataAnlytics, #CloudDataProc, #MachineLearning, #CloudDatalab
Differences: All three providers offer similar building blocks; data processing, data orchestration, streaming analytics, machine learning and visualisations. AWS certainly has all the bases covered with a solid set of products that will meet most needs. Azure offers a comprehensive and impressive suite of managed analytical products. They support open source big data solutions alongside new serverless analytical products such as Data Lake. Google provide their own twist to cloud analytics with their range of services. With Dataproc and Dataflow, Google have a strong core to their proposition. Tensorflow has been getting a lot of attention recently and there will be many who will be keen to see Machine Learning come out of preview.
8
Category: Virtual servers
Description:Virtual servers allow users to deploy, manage, and maintain OS and server software. Instance types provide combinations of CPU/RAM. Users pay for what they use with the flexibility to change sizes.
Batch: Run large-scale parallel and high-performance computing applications efficiently in the cloud.
References:
[AWS]:Elastic Compute Cloud (EC2), Amazon Bracket(Explore and experiment with quantum computing), Amazon Ec2 M6g Instances (Achieve up to 40% better price performance), Amazon Ec2 Inf1 instancs (Deliver cost-effective ML inference), AWS Graviton2 Processors (Optimize price performance for cloud workloads), AWS Batch, AWS AutoScaling, VMware Cloud on AWS, AWS Local Zones (Run low latency applications at the edge), AWS Wavelength (Deliver ultra-low latency applications for 5G devices), AWS Nitro Enclaves (Further protect highly sensitive data), AWS Outposts (Run AWS infrastructure and services on-premises)
[Azure]:Azure Virtual Machines, Azure Batch, Virtual Machine Scale Sets, Azure VMware by CloudSimple
[Google]:Compute Engine, Preemptible Virtual Machines, Managed instance groups (MIGs), Google Cloud VMware Solution by CloudSimple
Tags: #AWSEC2, #AWSBatch, #AWSAutoscaling, #AzureVirtualMachine, #AzureBatch, #VirtualMachineScaleSets, #AzureVMWare, #ComputeEngine, #MIGS, #VMWare
Differences: There is very little to choose between the 3 providers when it comes to virtual servers. Amazon has some impressive high end kit, on the face of it this sound like it would make AWS a clear winner. However, if your only option is to choose the biggest box available you will need to make sure you have very deep pockets, and perhaps your money may be better spent re-architecting your apps for horizontal scale.Azure’s remains very strong in the PaaS space and now has a IaaS that can genuinely compete with AWS
Google offers a simple and very capable set of services that are easy to understand. However, with availability in only 5 regions it does not have the coverage of the other players.
9
Category: Containers and container orchestrators
Description: A container is a standard unit of software that packages up code and all its dependencies so the application runs quickly and reliably from one computing environment to another.
Container orchestration is all about managing the lifecycles of containers, especially in large, dynamic environments.
References:
[AWS]:EC2 Container Service (ECS), Fargate(Run containers without anaging servers or clusters), EC2 Container Registry(managed AWS Docker registry service that is secure, scalable, and reliable.), Elastic Container Service for Kubernetes (EKS: runs the Kubernetes management infrastructure across multiple AWS Availability Zones), App Mesh( application-level networking to make it easy for your services to communicate with each other across multiple types of compute infrastructure)
[Azure]:Azure Container Instances, Azure Container Registry, Azure Kubernetes Service (AKS), Service Fabric Mesh
[Google]:Google Container Engine, Container Registry, Kubernetes Engine
Tags:#ECS, #Fargate, #EKS, #AppMesh, #ContainerEngine, #ContainerRegistry, #AKS
Differences: Google Container Engine, AWS Container Services, and Azure Container Instances can be used to run docker containers. Google offers a simple and very capable set of services that are easy to understand. However, with availability in only 5 regions it does not have the coverage of the other players.
10
Category: Serverless
Description: Integrate systems and run backend processes in response to events or schedules without provisioning or managing servers.
References:
[AWS]:AWS Lambda
[Azure]:Azure Functions
[Google]:Google Cloud Functions
Tags:#AWSLAmbda, #AzureFunctions, #GoogleCloudFunctions
Differences: Both AWS Lambda and Microsoft Azure Functions and Google Cloud Functions offer dynamic, configurable triggers that you can use to invoke your functions on their platforms. AWS Lambda, Azure and Google Cloud Functions support Node.js, Python, and C#. The beauty of serverless development is that, with minor changes, the code you write for one service should be portable to another with little effort – simply modify some interfaces, handle any input/output transforms, and an AWS Lambda Node.JS function is indistinguishable from a Microsoft Azure Node.js Function. AWS Lambda provides further support for Python and Java, while Azure Functions provides support for F# and PHP. AWS Lambda is built from the AMI, which runs on Linux, while Microsoft Azure Functions run in a Windows environment. AWS Lambda uses the AWS Machine architecture to reduce the scope of containerization, letting you spin up and tear down individual pieces of functionality in your application at will.
11
Category: Relational databases
Description: Managed relational database service where resiliency, scale, and maintenance are primarily handled by the platform.
References:
[AWS]:AWS RDS(MySQL and PostgreSQL-compatible relational database built for the cloud,), Aurora(MySQL and PostgreSQL-compatible relational database built for the cloud)
[Azure]:SQL Database, Azure Database for MySQL, Azure Database for PostgreSQL
[Google]:Cloud SQL
Tags: #AWSRDS, #AWSAUrora, #AzureSQlDatabase, #AzureDatabaseforMySQL, #GoogleCloudSQL
Differences: All three providers boast impressive relational database offering. RDS supports an impressive range of managed relational stores while Azure SQL Database is probably the most advanced managed relational database available today. Azure also has the best out-of-the-box support for cross-region geo-replication across its database offerings.
12
Category: NoSQL, Document Databases
Description:A globally distributed, multi-model database that natively supports multiple data models: key-value, documents, graphs, and columnar.
References:
[AWS]:DynamoDB (key-value and document database that delivers single-digit millisecond performance at any scale.), SimpleDB ( a simple web services interface to create and store multiple data sets, query your data easily, and return the results.), Managed Cassandra Services(MCS)
[Azure]:Table Storage, DocumentDB, Azure Cosmos DB
[Google]:Cloud Datastore (handles sharding and replication in order to provide you with a highly available and consistent database. )
Tags:#AWSDynamoDB, #SimpleDB, #TableSTorage, #DocumentDB, AzureCosmosDB, #GoogleCloudDataStore
Differences:DynamoDB and Cloud Datastore are based on the document store database model and are therefore similar in nature to open-source solutions MongoDB and CouchDB. In other words, each database is fundamentally a key-value store. With more workloads moving to the cloud the need for NoSQL databases will become ever more important, and again all providers have a good range of options to satisfy most performance/cost requirements. Of all the NoSQL products on offer it’s hard not to be impressed by DocumentDB; Azure also has the best out-of-the-box support for cross-region geo-replication across its database offerings.
13
Category:Caching
Description:An in-memory–based, distributed caching service that provides a high-performance store typically used to offload non transactional work from a database.
References:
[AWS]:AWS ElastiCache (works as an in-memory data store and cache to support the most demanding applications requiring sub-millisecond response times.)
[Azure]:Azure Cache for Redis (based on the popular software Redis. It is typically used as a cache to improve the performance and scalability of systems that rely heavily on backend data-stores.)
[Google]:Memcache (In-memory key-value store, originally intended for caching)
Tags:#Redis, #Memcached
<Differences: They all support horizontal scaling via sharding.They all improve the performance of web applications by allowing you to retrive information from fast, in-memory caches, instead of relying on slower disk-based databases.”, “Differences”: “ElastiCache supports Memcached and Redis. Memcached Cloud provides various data persistence options as well as remote backups for disaster recovery purposes. Redis offers persistence to disk, Memcache does not. This can be very helpful if you cache lots of data, since you remove the slowness around having a fully cold cache. Redis also offers several extra data structures that Memcache doesn’t— Lists, Sets, Sorted Sets, etc. Memcache only has Key/Value pairs. Memcache is multi-threaded. Redis is single-threaded and event driven. Redis is very fast, but it’ll never be multi-threaded. At hight scale, you can squeeze more connections and transactions out of Memcache. Memcache tends to be more memory efficient. This can make a big difference around the magnitude of 10s of millions or 100s of millions of keys. ElastiCache supports Memcached and Redis. Memcached Cloud provides various data persistence options as well as remote backups for disaster recovery purposes. Redis offers persistence to disk, Memcache does not. This can be very helpful if you cache lots of data, since you remove the slowness around having a fully cold cache. Redis also offers several extra data structures that Memcache doesn’t— Lists, Sets, Sorted Sets, etc. Memcache only has Key/Value pairs. Memcache is multi-threaded. Redis is single-threaded and event driven. Redis is very fast, but it’ll never be multi-threaded. At hight scale, you can squeeze more connections and transactions out of Memcache. Memcache tends to be more memory efficient. This can make a big difference around the magnitude of 10s of millions or 100s of millions of keys.
14
Category: Security, identity, and access
Description:Authentication and authorization: Allows users to securely control access to services and resources while offering data security and protection. Create and manage users and groups, and use permissions to allow and deny access to resources.
References:
[AWS]:Identity and Access Management (IAM), AWS Organizations, Multi-Factor Authentication, AWS Directory Service, Cognito(provides solutions to control access to backend resources from your app), Amazon Detective (Investigate potential security issues), AWS IAM Access Analyzer(Easily analyze resource accessibility)
[Azure]:Azure Active Directory, Azure Subscription Management + Azure RBAC, Multi-Factor Authentication, Azure Active Directory Domain Services, Azure Active Directory B2C, Azure Policy, Management Groups
[Google]:Cloud Identity, Identity Platform, Cloud IAM, Policy Intelligence, Cloud Resource Manager, Cloud Identity-Aware Proxy, Context-aware accessManaged Service for Microsoft Active Directory, Security key enforcement, Titan Security Key
Tags: #IAM, #AWSIAM, #AzureIAM, #GoogleIAM, #Multi-factorAuthentication
Differences: One unique thing about AWS IAM is that accounts created in the organization (not through federation) can only be used within that organization. This contrasts with Google and Microsoft. On the good side, every organization is self-contained. On the bad side, users can end up with multiple sets of credentials they need to manage to access different organizations. The second unique element is that every user can have a non-interactive account by creating and using access keys, an interactive account by enabling console access, or both. (Side note: To use the CLI, you need to have access keys generated.)
15
Category: Object Storage and Content delivery
Description:Object storage service, for use cases including cloud applications, content distribution, backup, archiving, disaster recovery, and big data analytics.
References:
[AWS]:Simple Storage Services (S3), Import/Export(used to move large amounts of data into and out of the Amazon Web Services public cloud using portable storage devices for transport.), Snowball( petabyte-scale data transport solution that uses devices designed to be secure to transfer large amounts of data into and out of the AWS Cloud), CloudFront( content delivery network (CDN) is massively scaled and globally distributed), Elastic Block Store (EBS: high performance block storage service), Elastic File System(shared, elastic file storage system that grows and shrinks as you add and remove files.), S3 Infrequent Access (IA: is for data that is accessed less frequently, but requires rapid access when needed. ), S3 Glacier( long-term storage of data that is infrequently accessed and for which retrieval latency times of 3 to 5 hours are acceptable.), AWS Backup( makes it easy to centralize and automate the back up of data across AWS services in the cloud as well as on-premises using the AWS Storage Gateway.), Storage Gateway(hybrid cloud storage service that gives you on-premises access to virtually unlimited cloud storage), AWS Import/Export Disk(accelerates moving large amounts of data into and out of AWS using portable storage devices for transport)
[Azure]:Azure Blob storage, File Storage, Data Lake Store, Azure Backup, Azure managed disks, Azure Files, Azure Storage cool tier, Azure Storage archive access tier, Azure Backup, StorSimple, Import/Export
[Google]:Cloud Storage, GlusterFS, CloudCDN
Tags:#S3, #AzureBlobStorage, #CloudStorage
Differences:Source: All providers have good object storage options and so storage alone is unlikely to be a deciding factor when choosing a cloud provider. The exception perhaps is for hybrid scenarios, in this case Azure and AWS clearly win. AWS and Google’s support for automatic versioning is a great feature that is currently missing from Azure; however Microsoft’s fully managed Data Lake Store offers an additional option that will appeal to organisations who are looking to run large scale analytical workloads. If you are prepared to wait 4 hours for your data and you have considerable amounts of the stuff then AWS Glacier storage might be a good option. If you use the common programming patterns for atomic updates and consistency, such as etags and the if-match family of headers, then you should be aware that AWS does not support them, though Google and Azure do. Azure also supports blob leasing, which can be used to provide a distributed lock.
16
Category:Internet of things (IoT)
Description:A cloud gateway for managing bidirectional communication with billions of IoT devices, securely and at scale. Deploy cloud intelligence directly on IoT devices to run in on-premises scenarios.
References:
[AWS]:AWS IoT (Internet of Things), AWS Greengrass, Kinesis Firehose, Kinesis Streams, AWS IoT Things Graph
[Azure]:Azure IoT Hub, Azure IoT Edge, Event Hubs, Azure Digital Twins, Azure Sphere
[Google]:Google Cloud IoT Core, Firebase, Brillo, Weave, CLoud Pub/SUb, Stream Analysis, Big Query, Big Query Streaming API
Tags:#IoT, #InternetOfThings, #Firebase
Differences:AWS and Azure have a more coherent message with their products clearly integrated into their respective platforms, whereas Google Firebase feels like a distinctly separate product.
17
Category:Web Applications
Description:Managed hosting platform providing easy to use services for deploying and scaling web applications and services. API Gateway is a a turnkey solution for publishing APIs to external and internal consumers. Cloudfront is a global content delivery network that delivers audio, video, applications, images, and other files.
References:
[AWS]:Elastic Beanstalk (for deploying and scaling web applications and services developed with Java, .NET, PHP, Node.js, Python, Ruby, Go, and Docker on familiar servers such as Apache, Nginx, Passenger, and IIS), AWS Wavelength (for delivering ultra-low latency applications for 5G), API Gateway (makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale.), CloudFront (web service that speeds up distribution of your static and dynamic web content, such as .html, .css, .js, and image files, to your users. CloudFront delivers your content through a worldwide network of data centers called edge locations.),Global Accelerator ( improves the availability and performance of your applications with local or global users. It provides static IP addresses that act as a fixed entry point to your application endpoints in a single or multiple AWS Regions, such as your Application Load Balancers, Network Load Balancers or Amazon EC2 instances.)AWS AppSync (simplifies application development by letting you create a flexible API to securely access, manipulate, and combine data from one or more data sources: GraphQL service with real-time data synchronization and offline programming features. )
[Azure]:App Service, API Management, Azure Content Delivery Network, Azure Content Delivery Network
[Google]:App Engine, Cloud API, Cloud Enpoint, APIGee
Tags: #AWSElasticBeanstalk, #AzureAppService, #GoogleAppEngine, #CloudEnpoint, #CloudFront, #APIgee
Differences: With AWS Elastic Beanstalk, developers retain full control over the AWS resources powering their application. If developers decide they want to manage some (or all) of the elements of their infrastructure, they can do so seamlessly by using Elastic Beanstalk’s management capabilities. AWS Elastic Beanstalk integrates with more apps than Google App Engines (Datadog, Jenkins, Docker, Slack, Github, Eclipse, etc..). Google App Engine has more features than AWS Elastic BEanstalk (App Identity, Java runtime, Datastore, Blobstore, Images, Go Runtime, etc..). Developers describe Amazon API Gateway as “Create, publish, maintain, monitor, and secure APIs at any scale”. Amazon API Gateway handles all the tasks involved in accepting and processing up to hundreds of thousands of concurrent API calls, including traffic management, authorization and access control, monitoring, and API version management. On the other hand, Google Cloud Endpoints is detailed as “Develop, deploy and manage APIs on any Google Cloud backend”. An NGINX-based proxy and distributed architecture give unparalleled performance and scalability. Using an Open API Specification or one of our API frameworks, Cloud Endpoints gives you the tools you need for every phase of API development and provides insight with Google Cloud Monitoring, Cloud Trace, Google Cloud Logging and Cloud Trace.
18
Category:Encryption
Description:Helps you protect and safeguard your data and meet your organizational security and compliance commitments.
References:
[AWS]:Key Management Service AWS KMS, CloudHSM
[Azure]:Key Vault
[Google]:Encryption By Default at Rest, Cloud KMS
Tags:#AWSKMS, #Encryption, #CloudHSM, #EncryptionAtRest, #CloudKMS
Differences: AWS KMS, is an ideal solution for organizations that want to manage encryption keys in conjunction with other AWS services. In contrast to AWS CloudHSM, AWS KMS provides a complete set of tools to manage encryption keys, develop applications and integrate with other AWS services. Google and Azure offer 4096 RSA. AWS and Google offer 256 bit AES. With AWs, you can bring your own key
19
Category:Internet of things (IoT)
Description:A cloud gateway for managing bidirectional communication with billions of IoT devices, securely and at scale. Deploy cloud intelligence directly on IoT devices to run in on-premises scenarios.
References:
[AWS]:AWS IoT, AWS Greengrass, Kinesis Firehose ( captures and loads streaming data in storage and business intelligence (BI) tools to enable near real-time analytics in the AWS cloud), Kinesis Streams (for rapid and continuous data intake and aggregation.), AWS IoT Things Graph (makes it easy to visually connect different devices and web services to build IoT applications.)
[Azure]:Azure IoT Hub, Azure IoT Edge, Event Hubs, Azure Digital Twins, Azure Sphere
[Google]:Google Cloud IoT Core, Firebase, Brillo, Weave, CLoud Pub/SUb, Stream Analysis, Big Query, Big Query Streaming API
Tags:#IoT, #InternetOfThings, #Firebase
Differences:AWS and Azure have a more coherent message with their products clearly integrated into their respective platforms, whereas Google Firebase feels like a distinctly separate product.
20
Category:Object Storage and Content delivery
Description: Object storage service, for use cases including cloud applications, content distribution, backup, archiving, disaster recovery, and big data analytics.
References:
[AWS]:Simple Storage Services (S3), Import/Export Snowball, CloudFront, Elastic Block Store (EBS), Elastic File System, S3 Infrequent Access (IA), S3 Glacier, AWS Backup, Storage Gateway, AWS Import/Export Disk, Amazon S3 Access Points(Easily manage access for shared data)
[Azure]:Azure Blob storage, File Storage, Data Lake Store, Azure Backup, Azure managed disks, Azure Files, Azure Storage cool tier, Azure Storage archive access tier, Azure Backup, StorSimple, Import/Export
[Google]:Cloud Storage, GlusterFS, CloudCDN
Tags:#S3, #AzureBlobStorage, #CloudStorage
Differences:All providers have good object storage options and so storage alone is unlikely to be a deciding factor when choosing a cloud provider. The exception perhaps is for hybrid scenarios, in this case Azure and AWS clearly win. AWS and Google’s support for automatic versioning is a great feature that is currently missing from Azure; however Microsoft’s fully managed Data Lake Store offers an additional option that will appeal to organisations who are looking to run large scale analytical workloads. If you are prepared to wait 4 hours for your data and you have considerable amounts of the stuff then AWS Glacier storage might be a good option. If you use the common programming patterns for atomic updates and consistency, such as etags and the if-match family of headers, then you should be aware that AWS does not support them, though Google and Azure do. Azure also supports blob leasing, which can be used to provide a distributed lock.
21
Category: Backend process logic
Description: Cloud technology to build distributed applications using out-of-the-box connectors to reduce integration challenges. Connect apps, data and devices on-premises or in the cloud.
References:
[AWS]:AWS Step Functions ( lets you build visual workflows that enable fast translation of business requirements into technical requirements. You can build applications in a matter of minutes, and when needs change, you can swap or reorganize components without customizing any code.)
[Azure]:Logic Apps (cloud service that helps you schedule, automate, and orchestrate tasks, business processes, and workflows when you need to integrate apps, data, systems, and services across enterprises or organizations.)
[Google]:Dataflow ( fully managed service for executing Apache Beam pipelines within the Google Cloud Platform ecosystem.)
Tags:#AWSStepFunctions, #LogicApps, #Dataflow
Differences: AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly. AWS Step Functions belongs to \”Cloud Task Management\” category of the tech stack, while Google Cloud Dataflow can be primarily classified under \”Real-time Data Processing\”. According to the StackShare community, Google Cloud Dataflow has a broader approval, being mentioned in 32 company stacks & 8 developers stacks; compared to AWS Step Functions, which is listed in 19 company stacks and 7 developer stacks.
22
Category: Enterprise application services
Description:Fully integrated Cloud service providing communications, email, document management in the cloud and available on a wide variety of devices.
References:
[AWS]:Amazon WorkMail, Amazon WorkDocs, Amazon Kendra (Sync and Index)
[Azure]:Office 365
[Google]:G Suite
Tags: #AmazonWorkDocs, #Office365, #GoogleGSuite
Differences: G suite document processing applications like Google Docs are far behind Office 365 popular Word and Excel software, but G Suite User interface is intuite, simple and easy to navigate. Office 365 is too clunky. Get 20% off G-Suite Business Plan with Promo Code: PCQ49CJYK7EATNC
23
Category: Networking
Description: Provides an isolated, private environment in the cloud. Users have control over their virtual networking environment, including selection of their own IP address range, creation of subnets, and configuration of route tables and network gateways.
References:
[AWS]:Virtual Private Cloud (VPC), Cloud virtual networking, Subnets, Elastic Network Interface (ENI), Route Tables, Network ACL, Secutity Groups, Internet Gateway, NAT Gateway, AWS VPN Gateway, AWS Route 53, AWS Direct Connect, AWS Network Load Balancer, VPN CloudHub, AWS Local Zones, AWS Transit Gateway network manager (Centrally manage global networks)
[Azure]:Virtual Network(provide services for building networks within Azure.),Subnets (network resources can be grouped by subnet for organisation and security.), Network Interface (Each virtual machine can be assigned one or more network interfaces (NICs)), Network Security Groups (NSG: contains a set of prioritised ACL rules that explicitly grant or deny access), Azure VPN Gateway ( allows connectivity to on-premise networks), Azure DNS, Traffic Manager (DNS based traffic routing solution.), ExpressRoute (provides connections up to 10 Gbps to Azure services over a dedicated fibre connection), Azure Load Balancer, Network Peering, Azure Stack (Azure Stack allows organisations to use Azure services running in private data centers.), Azure Load Balancer , Azure Log Analytics, Azure DNS,
[Google]:Cloud Virtual Network, Subnets, Network Interface, Protocol fowarding, Cloud VPN, Cloud DNS, Virtual Private Network, Cloud Interconnect, CDN interconnect, Cloud DNS, Stackdriver, Google Cloud Load Balancing,
Tags:#VPC, #Subnets, #ACL, #VPNGateway, #CloudVPN, #NetworkInterface, #ENI, #RouteTables, #NSG, #NetworkACL, #InternetGateway, #NatGateway, #ExpressRoute, #CloudInterConnect, #StackDriver
Differences: Subnets group related resources, however, unlike AWS and Azure, Google do not constrain the private IP address ranges of subnets to the address space of the parent network. Like Azure, Google has a built in internet gateway that can be specified from routing rules.
24
Category: Management
Description: A unified management console that simplifies building, deploying, and operating your cloud resources.
References:
[AWS]: AWS Management Console, Trusted Advisor, AWS Usage and Billing Report, AWS Application Discovery Service, Amazon EC2 Systems Manager, AWS Personal Health Dashboard, AWS Compute Optimizer (Identify optimal AWS Compute resources)
[Azure]:Azure portal, Azure Advisor, Azure Billing API, Azure Migrate, Azure Monitor, Azure Resource Health
[Google]:Google CLoud Platform, Cost Management, Security Command Center, StackDriver
Tags: #AWSConsole, #AzurePortal, #GoogleCloudConsole, #TrustedAdvisor, #AzureMonitor, #SecurityCommandCenter
Differences: AWS Console categorizes its Infrastructure as a Service offerings into Compute, Storage and Content Delivery Network (CDN), Database, and Networking to help businesses and individuals grow. Azure excels in the Hybrid Cloud space allowing companies to integrate onsite servers with cloud offerings. Google has a strong offering in containers, since Google developed the Kubernetes standard that AWS and Azure now offer. GCP specializes in high compute offerings like Big Data, analytics and machine learning. It also offers considerable scale and load balancing – Google knows data centers and fast response time.
25
Category: DevOps and application monitoring
Description: Comprehensive solution for collecting, analyzing, and acting on telemetry from your cloud and on-premises environments; Cloud services for collaborating on code development; Collection of tools for building, debugging, deploying, diagnosing, and managing multiplatform scalable apps and services; Fully managed build service that supports continuous integration and deployment.
References:
[AWS]:AWS CodePipeline(orchestrates workflow for continuous integration, continuous delivery, and continuous deployment), AWS CloudWatch (monitor your AWS resources and the applications you run on AWS in real time. ), AWS X-Ray (application performance management service that enables a developer to analyze and debug applications in aws), AWS CodeDeploy (automates code deployments to Elastic Compute Cloud (EC2) and on-premises servers. ), AWS CodeCommit ( source code storage and version-control service), AWS Developer Tools, AWS CodeBuild (continuous integration service that compiles source code, runs tests, and produces software packages that are ready to deploy. ), AWS Command Line Interface (unified tool to manage your AWS services), AWS OpsWorks (Chef-based), AWS CloudFormation ( provides a common language for you to describe and provision all the infrastructure resources in your cloud environment.), Amazon CodeGuru (for automated code reviews and application performance recommendations)
[Azure]:Azure Monitor, Azure DevOps, Azure Developer Tools, Azure CLI Azure PowerShell, Azure Automation, Azure Resource Manager , VM extensions , Azure Automation
[Google]:DevOps Solutions (Infrastructure as code, Configuration management, Secrets management, Serverless computing, Continuous delivery, Continuous integration , Stackdriver (combines metrics, logs, and metadata from all of your cloud accounts and projects into a single comprehensive view of your environment)
Tags: #CloudWatch, #StackDriver, #AzureMonitor, #AWSXray, #AWSCodeDeploy, #AzureDevOps, #GoogleDevopsSolutions
Differences: CodeCommit eliminates the need to operate your own source control system or worry about scaling its infrastructure. Azure DevOps provides unlimited private Git hosting, cloud build for continuous integration, agile planning, and release management for continuous delivery to the cloud and on-premises. Includes broad IDE support.
SageMaker | Azure Machine Learning Studio
A collaborative, drag-and-drop tool to build, test, and deploy predictive analytics solutions on your data.
Alexa Skills Kit | Microsoft Bot Framework
Build and connect intelligent bots that interact with your users using text/SMS, Skype, Teams, Slack, Office 365 mail, Twitter, and other popular services.
API capable of converting speech to text, understanding intent, and converting text back to speech for natural responsiveness.
Amazon Lex | Language Understanding (LUIS)
Allows your applications to understand user commands contextually.
Amazon Polly, Amazon Transcribe | Azure Speech Services
Enables both Speech to Text, and Text into Speech capabilities.
The Speech Services are the unification of speech-to-text, text-to-speech, and speech-translation into a single Azure subscription. It’s easy to speech enable your applications, tools, and devices with the Speech SDK, Speech Devices SDK, or REST APIs.
Amazon Polly is a Text-to-Speech (TTS) service that uses advanced deep learning technologies to synthesize speech that sounds like a human voice. With dozens of lifelike voices across a variety of languages, you can select the ideal voice and build speech-enabled applications that work in many different countries.
Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to their applications. Using the Amazon Transcribe API, you can analyze audio files stored in Amazon S3 and have the service return a text file of the transcribed speech.
Amazon Rekognition | Cognitive Services
Computer Vision: Extract information from images to categorize and process visual data.
Amazon Rekognition is a simple and easy to use API that can quickly analyze any image or video file stored in Amazon S3. Amazon Rekognition is always learning from new data, and we are continually adding new labels and facial recognition features to the service.
Face: Detect, identy, and analyze faces in photos.
Emotions: Recognize emotions in images.
Alexa Skill Set | Azure Virtual Assistant
The Virtual Assistant Template brings together a number of best practices we’ve identified through the building of conversational experiences and automates integration of components that we’ve found to be highly beneficial to Bot Framework developers.
Big data and analytics
Data warehouse
AWS Redshift | SQL Data Warehouse
Cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP) to quickly run complex queries across petabytes of data.
Big data processing
EMR | Azure Databricks
Apache Spark-based analytics platform.
Managed Hadoop service. Deploy and manage Hadoop clusters in Azure.
Data orchestration / ETL
AWS Data Pipeline, AWS Glue | Data Factory
Processes and moves data between different compute and storage services, as well as on-premises data sources at specified intervals. Create, schedule, orchestrate, and manage data pipelines.
A fully managed service that serves as a system of registration and system of discovery for enterprise data sources
Analytics and visualization
AWS Kinesis Analytics | Stream Analytics
Data Lake Analytics | Data Lake Store
Storage and analysis platforms that create insights from large quantities of data, or data that originates from many sources.
Business intelligence tools that build visualizations, perform ad hoc analysis, and develop business insights from data.
Delivers full-text search and related search analytics and capabilities.
Amazon Athena | Azure Data Lake Analytics
Provides a serverless interactive query service that uses standard SQL for analyzing databases.
Compute
Virtual servers
Elastic Compute Cloud (EC2) | Azure Virtual Machines
Virtual servers allow users to deploy, manage, and maintain OS and server software. Instance types provide combinations of CPU/RAM. Users pay for what they use with the flexibility to change sizes.
Run large-scale parallel and high-performance computing applications efficiently in the cloud.
AWS Auto Scaling | Virtual Machine Scale Sets
Allows you to automatically change the number of VM instances. You set defined metric and thresholds that determine if the platform adds or removes instances.
VMware Cloud on AWS | Azure VMware by CloudSimple
Redeploy and extend your VMware-based enterprise workloads to Azure with Azure VMware Solution by CloudSimple. Keep using the VMware tools you already know to manage workloads on Azure without disrupting network, security, or data protection policies.
Containers and container orchestrators
EC2 Container Service (ECS), Fargate | Azure Container Instances
Azure Container Instances is the fastest and simplest way to run a container in Azure, without having to provision any virtual machines or adopt a higher-level orchestration service.
EC2 Container Registry | Azure Container Registry
Allows customers to store Docker formatted images. Used to create all types of container deployments on Azure.
Elastic Container Service for Kubernetes (EKS) | Azure Kubernetes Service (AKS)
Deploy orchestrated containerized applications with Kubernetes. Simplify monitoring and cluster management through auto upgrades and a built-in operations console.
App Mesh | Service Fabric Mesh
Fully managed service that enables developers to deploy microservices applications without managing virtual machines, storage, or networking.
AWS App Mesh is a service mesh that provides application-level networking to make it easy for your services to communicate with each other across multiple types of compute infrastructure. App Mesh standardizes how your services communicate, giving you end-to-end visibility and ensuring high-availability for your applications.
Serverless
Integrate systems and run backend processes in response to events or schedules without provisioning or managing servers.
AWS Lambda is an event-driven, serverless computing platform provided by Amazon as a part of the Amazon Web Services. It is a computing service that runs code in response to events and automatically manages the computing resources required by that code
Database
Relational database
AWS RDS | SQL Database Azure Database for MySQL Azure Database for PostgreSQL
Managed relational database service where resiliency, scale, and maintenance are primarily handled by the platform.
Amazon Relational Database Service is a distributed relational database service by Amazon Web Services. It is a web service running “in the cloud” designed to simplify the setup, operation, and scaling of a relational database for use in applications. Administration processes like patching the database software, backing up databases and enabling point-in-time recovery are managed automatically. Scaling storage and compute resources can be performed by a single API call as AWS does not offer an ssh connection to RDS instances.
NoSQL / Document
DynamoDB and SimpleDB | Azure Cosmos DB
A globally distributed, multi-model database that natively supports multiple data models: key-value, documents, graphs, and columnar.
Caching
AWS ElastiCache | Azure Cache for Redis
An in-memory–based, distributed caching service that provides a high-performance store typically used to offload non transactional work from a database.
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. ElastiCache supports two open-source in-memory caching engines: Memcached and Redis.
Database migration
AWS Database Migration Service | Azure Database Migration Service
Migration of database schema and data from one database format to a specific database technology in the cloud.
AWS Database Migration Service helps you migrate databases to AWS quickly and securely. The source database remains fully operational during the migration, minimizing downtime to applications that rely on the database. The AWS Database Migration Service can migrate your data to and from most widely used commercial and open-source databases.
DevOps and application monitoring
AWS CloudWatch, AWS X-Ray | Azure Monitor
Comprehensive solution for collecting, analyzing, and acting on telemetry from your cloud and on-premises environments.
Amazon CloudWatch is a monitoring and observability service built for DevOps engineers, developers, site reliability engineers (SREs), and IT managers. CloudWatch provides you with data and actionable insights to monitor your applications, respond to system-wide performance changes, optimize resource utilization, and get a unified view of operational health. CloudWatch collects monitoring and operational data in the form of logs, metrics, and events, providing you with a unified view of AWS resources, applications, and services that run on AWS and on-premises servers.
AWS X-Ray is an application performance management service that enables a developer to analyze and debug applications in the Amazon Web Services (AWS) public cloud. A developer can use AWS X-Ray to visualize how a distributed application is performing during development or production, and across multiple AWS regions and accounts.
AWS CodeDeploy, AWS CodeCommit, AWS CodePipeline | Azure DevOps
A cloud service for collaborating on code development.
AWS CodeDeploy is a fully managed deployment service that automates software deployments to a variety of compute services such as Amazon EC2, AWS Fargate, AWS Lambda, and your on-premises servers. AWS CodeDeploy makes it easier for you to rapidly release new features, helps you avoid downtime during application deployment, and handles the complexity of updating your applications.
AWS CodePipeline is a fully managed continuous delivery service that helps you automate your release pipelines for fast and reliable application and infrastructure updates. CodePipeline automates the build, test, and deploy phases of your release process every time there is a code change, based on the release model you define.
AWS CodeCommit is a source code storage and version-control service for Amazon Web Services’ public cloud customers. CodeCommit was designed to help IT teams collaborate on software development, including continuous integration and application delivery.
AWS Developer Tools | Azure Developer Tools
Collection of tools for building, debugging, deploying, diagnosing, and managing multiplatform scalable apps and services.
The AWS Developer Tools are designed to help you build software like Amazon. They facilitate practices such as continuous delivery and infrastructure as code for serverless, containers, and Amazon EC2.
AWS CodeBuild | Azure DevOps
Fully managed build service that supports continuous integration and deployment.
AWS Command Line Interface | Azure CLI Azure PowerShell
Built on top of the native REST API across all cloud services, various programming language-specific wrappers provide easier ways to create solutions.
The AWS Command Line Interface (CLI) is a unified tool to manage your AWS services. With just one tool to download and configure, you can control multiple AWS services from the command line and automate them through scripts.
AWS OpsWorks (Chef-based) | Azure Automation
Configures and operates applications of all shapes and sizes, and provides templates to create and manage a collection of resources.
AWS OpsWorks is a configuration management service that provides managed instances of Chef and Puppet. Chef and Puppet are automation platforms that allow you to use code to automate the configurations of your servers.
AWS CloudFormation | Azure Resource Manager , VM extensions , Azure Automation
Provides a way for users to automate the manual, long-running, error-prone, and frequently repeated IT tasks.
AWS CloudFormation provides a common language for you to describe and provision all the infrastructure resources in your cloud environment. CloudFormation allows you to use a simple text file to model and provision, in an automated and secure manner, all the resources needed for your applications across all regions and accounts.
Networking
Area
Cloud virtual networking, Virtual Private Cloud (VPC) | Virtual Network
Provides an isolated, private environment in the cloud. Users have control over their virtual networking environment, including selection of their own IP address range, creation of subnets, and configuration of route tables and network gateways.
Cross-premises connectivity
AWS VPN Gateway | Azure VPN Gateway
Connects Azure virtual networks to other Azure virtual networks, or customer on-premises networks (Site To Site). Allows end users to connect to Azure services through VPN tunneling (Point To Site).
DNS management
AWS Route 53 | Azure DNS
Manage your DNS records using the same credentials and billing and support contract as your other Azure services
Route 53 | Traffic Manager
A service that hosts domain names, plus routes users to Internet applications, connects user requests to datacenters, manages traffic to apps, and improves app availability with automatic failover.
Dedicated network
AWS Direct Connect | ExpressRoute
Establishes a dedicated, private network connection from a location to the cloud provider (not over the Internet).
Load balancing
AWS Network Load Balancer | Azure Load Balancer
Azure Load Balancer load-balances traffic at layer 4 (TCP or UDP).
Application Load Balancer | Application Gateway
Application Gateway is a layer 7 load balancer. It supports SSL termination, cookie-based session affinity, and round robin for load-balancing traffic.
Internet of things (IoT)
AWS IoT | Azure IoT Hub
A cloud gateway for managing bidirectional communication with billions of IoT devices, securely and at scale.
AWS Greengrass | Azure IoT Edge
Deploy cloud intelligence directly on IoT devices to run in on-premises scenarios.
Kinesis Firehose, Kinesis Streams | Event Hubs
Services that allow the mass ingestion of small data inputs, typically from devices and sensors, to process and route the data.
AWS IoT Things Graph | Azure Digital Twins
Azure Digital Twins is an IoT service that helps you create comprehensive models of physical environments. Create spatial intelligence graphs to model the relationships and interactions between people, places, and devices. Query data from a physical space rather than disparate sensors.
Management
Trusted Advisor | Azure Advisor
Provides analysis of cloud resource configuration and security so subscribers can ensure they’re making use of best practices and optimum configurations.
AWS Usage and Billing Report | Azure Billing API
Services to help generate, monitor, forecast, and share billing data for resource usage by time, organization, or product resources.
AWS Management Console | Azure portal
A unified management console that simplifies building, deploying, and operating your cloud resources.
AWS Application Discovery Service | Azure Migrate
Assesses on-premises workloads for migration to Azure, performs performance-based sizing, and provides cost estimations.
Amazon EC2 Systems Manager | Azure Monitor
Comprehensive solution for collecting, analyzing, and acting on telemetry from your cloud and on-premises environments.
AWS Personal Health Dashboard | Azure Resource Health
Provides detailed information about the health of resources as well as recommended actions for maintaining resource health.
Security, identity, and access
Authentication and authorization
Identity and Access Management (IAM) | Azure Active Directory
Allows users to securely control access to services and resources while offering data security and protection. Create and manage users and groups, and use permissions to allow and deny access to resources.
Identity and Access Management (IAM) | Azure Role Based Access Control
Role-based access control (RBAC) helps you manage who has access to Azure resources, what they can do with those resources, and what areas they have access to.
AWS Organizations | Azure Subscription Management + Azure RBAC
Security policy and role management for working with multiple accounts.
Multi-Factor Authentication | Multi-Factor Authentication
Safeguard access to data and applications while meeting user demand for a simple sign-in process.
AWS Directory Service | Azure Active Directory Domain Services
Provides managed domain services such as domain join, group policy, LDAP, and Kerberos/NTLM authentication that are fully compatible with Windows Server Active Directory.
Cognito | Azure Active Directory B2C
A highly available, global, identity management service for consumer-facing applications that scales to hundreds of millions of identities.
AWS Organizations | Azure Policy
Azure Policy is a service in Azure that you use to create, assign, and manage policies. These policies enforce different rules and effects over your resources, so those resources stay compliant with your corporate standards and service level agreements.
AWS Organizations | Management Groups
Azure management groups provide a level of scope above subscriptions. You organize subscriptions into containers called “management groups” and apply your governance conditions to the management groups. All subscriptions within a management group automatically inherit the conditions applied to the management group. Management groups give you enterprise-grade management at a large scale, no matter what type of subscriptions you have.
Encryption
Server-side encryption with Amazon S3 Key Management Service | Azure Storage Service Encryption
Helps you protect and safeguard your data and meet your organizational security and compliance commitments.
Key Management Service AWS KMS, CloudHSM | Key Vault
Provides security solution and works with other services by providing a way to manage, create, and control encryption keys stored in hardware security modules (HSM).
Firewall
Web Application Firewall | Application Gateway – Web Application Firewall
A firewall that protects web applications from common web exploits.
Web Application Firewall | Azure Firewall
Provides inbound protection for non-HTTP/S protocols, outbound network-level protection for all ports and protocols, and application-level protection for outbound HTTP/S.
Security
Inspector | Security Center
An automated security assessment service that improves the security and compliance of applications. Automatically assess applications for vulnerabilities or deviations from best practices.
Certificate Manager | App Service Certificates available on the Portal
Service that allows customers to create, manage, and consume certificates seamlessly in the cloud.
GuardDuty | Azure Advanced Threat Protection
Detect and investigate advanced attacks on-premises and in the cloud.
AWS Artifact | Service Trust Portal
Provides access to audit reports, compliance guides, and trust documents from across cloud services.
AWS Shield | Azure DDos Protection Service
Provides cloud services with protection from distributed denial of services (DDoS) attacks.
Storage
Object storage
Simple Storage Services (S3) | Azure Blob storage
Object storage service, for use cases including cloud applications, content distribution, backup, archiving, disaster recovery, and big data analytics.
Virtual server disks
Elastic Block Store (EBS) | Azure managed disks
SSD storage optimized for I/O intensive read/write operations. For use as high-performance Azure virtual machine storage.
Shared files
Elastic File System | Azure Files
Provides a simple interface to create and configure file systems quickly, and share common files. Can be used with traditional protocols that access files over a network.
Archiving and backup
S3 Infrequent Access (IA) | Azure Storage cool tier
Cool storage is a lower-cost tier for storing data that is infrequently accessed and long-lived.
S3 Glacier | Azure Storage archive access tier
Archive storage has the lowest storage cost and higher data retrieval costs compared to hot and cool storage.
AWS Backup | Azure Backup
Back up and recover files and folders from the cloud, and provide offsite protection against data loss.
Hybrid storage
Storage Gateway | StorSimple
Integrates on-premises IT environments with cloud storage. Automates data management and storage, plus supports disaster recovery.
Bulk data transfer
AWS Import/Export Disk | Import/Export
A data transport solution that uses secure disks and appliances to transfer large amounts of data. Also offers data protection during transit.
AWS Import/Export Snowball, Snowball Edge, Snowmobile | Azure Data Box
Petabyte- to exabyte-scale data transport solution that uses secure data storage devices to transfer large amounts of data to and from Azure.
Web applications
Elastic Beanstalk | App Service
Managed hosting platform providing easy to use services for deploying and scaling web applications and services.
API Gateway | API Management
A turnkey solution for publishing APIs to external and internal consumers.
CloudFront | Azure Content Delivery Network
A global content delivery network that delivers audio, video, applications, images, and other files.
Global Accelerator | Azure Front Door
Easily join your distributed microservice architectures into a single global application using HTTP load balancing and path-based routing rules. Automate turning up new regions and scale-out with API-driven global actions, and independent fault-tolerance to your back end microservices in Azure—or anywhere.
Miscellaneous
Backend process logic
AWS Step Functions | Logic Apps
Cloud technology to build distributed applications using out-of-the-box connectors to reduce integration challenges. Connect apps, data and devices on-premises or in the cloud.
Enterprise application services
Amazon WorkMail, Amazon WorkDocs | Office 365
Fully integrated Cloud service providing communications, email, document management in the cloud and available on a wide variety of devices.
Gaming
GameLift, GameSparks | PlayFab
Managed services for hosting dedicated game servers.
Media transcoding
Elastic Transcoder | Media Services
Services that offer broadcast-quality video streaming services, including various transcoding technologies.
Workflow
Simple Workflow Service (SWF) | Logic Apps
Serverless technology for connecting apps, data and devices anywhere, whether on-premises or in the cloud for large ecosystems of SaaS and cloud-based connectors.
Hybrid
Outposts | Azure Stack
Azure Stack is a hybrid cloud platform that enables you to run Azure services in your company’s or service provider’s datacenter. As a developer, you can build apps on Azure Stack. You can then deploy them to either Azure Stack or Azure, or you can build truly hybrid apps that take advantage of connectivity between an Azure Stack cloud and Azure.
Source: AWS to Azure services comparison – Azure Architecture
