These are just a few examples of how AWS services can be used in different industries
and scenarios. The possibilities are endless, and it's up to architects to determine the best way to leverage these services to meet the unique needs and goals of their organisations.
1. Using Amazon CloudFront to improve the performance and availability of static
content for a website
2. Using Amazon Route 53 to manage domain names and DNS records for a web
application
3. Using Amazon Kinesis to process and analyze streaming data in real-time
4. Using AWS CloudFormation to automate the deployment of infrastructure and
services
5. Using Amazon Elastic Container Service (ECS) to manage and scale containerized
applications
6. Using Amazon Simple Queue Service (SQS) to decouple and scale components of a
distributed system
7. Using AWS Step Functions to orchestrate and coordinate workflows across multiple
services
8. Using AWS Glue to automate and manage data ETL processes
9. Using Amazon Athena to perform ad-hoc queries on data stored in Amazon S3
10. Using Amazon Redshift to perform large-scale data warehousing and analytics
11. Using Amazon DynamoDB to store and query non-relational data with high
performance and scalability
12. Using AWS IoT to build and manage IoT devices and data processing pipelines
13. Using Amazon SageMaker to build and train machine learning models on AWS
14. Using AWS Lambda to implement serverless functions for event-driven processing
15. Using Amazon API Gateway to build and manage RESTful APIs for web applications
16. Using AWS CloudTrail to audit and monitor AWS API activity and user behavior
17. Using Amazon GuardDuty to detect and respond to security threats and
vulnerabilities
18. Using AWS Certificate Manager to manage SSL/TLS certificates for web applications
19. Using Amazon Elastic File System (EFS) to provide scalable and highly available file
storage for applications
20. Using AWS Elastic Beanstalk to deploy and manage web applications on AWS
21. Using AWS CodeCommit to store and manage source code repositories for
applications
22. Using AWS CodeDeploy to automate the deployment of applications to EC2
instances23. Using AWS CodePipeline to automate the continuous delivery of applications on
AWS
24. Using AWS OpsWorks to automate the configuration and management of
infrastructure and applications
25. Using Amazon MQ to manage and scale message brokers for distributed systems
26. Using Amazon ECR to store and manage Docker images for containerized
applications
27. Using AWS IoT Analytics to analyze and process IoT data at scale
28. Using Amazon WorkSpaces to provide cloud-based desktops for remote workers
29. Using AWS Direct Connect to establish a dedicated network connection between
on-premises infrastructure and AWS
30. Using AWS VPN to establish secure VPN connections between on-premises
infrastructure and AWS
31. Using AWS WAF to protect web applications from common web exploits and
attacks
32. Using AWS Shield to protect web applications from DDoS attacks
33. Using Amazon EMR to process large-scale data sets using Apache Hadoop and other
big data technologies
34. Using AWS Storage Gateway to connect on-premises storage infrastructure to AWS
35. Using Amazon Elastic Inference to accelerate deep learning inference using GPU
resources
36. Using AWS Firewall Manager to manage multiple AWS WAF and Shield resources
across accounts and regions
37. Using Amazon AppStream to stream desktop applications to users on any device
38. Using Amazon Managed Blockchain to create and manage scalable blockchain
networks
39. Using AWS PrivateLink to access AWS services over private network connections
40. Using Amazon Connect to provide cloud-based contact center solutions
41. Using Amazon Lex to build conversational interfaces and chatbots
42. Using AWS Organizations to manage multiple AWS accounts and resources across
an organization
43. Using Amazon Chime to provide secure and reliable video conferencing and
collaboration for remote teams
44. Using AWS Batch to run batch computing workloads on AWS
45. Using AWS Elemental MediaConvert to convert and transcode video files for
delivery to different devices and platforms
46. Using AWS Elemental MediaLive to encode and stream live video for broadcast and
online video platforms
47. Using AWS Elemental MediaPackage to prepare and deliver video content for
online video platforms
48. Using AWS Elemental MediaStore to store and retrieve video and media assets for
online video platforms
49. Using Amazon Elastic Transcoder to transcode media files for delivery to different
devices and platforms
50. Using AWS IoT Device Defender to monitor and secure IoT devices and data
51. Using Amazon Pinpoint to engage with customers through targeted and
personalized messaging
52. Using AWS X-Ray to analyze and debug distributed applications and services
53. Using Amazon Elasticache to provide in-memory caching for web applications and
services
54. Using AWS Cloud9 to develop and test code in a cloud-based IDE
55. Using Amazon Lex to build voice assistants and chatbots for customer service and
support
56. Using Amazon Polly to convert text to lifelike speech for applications and services
57. Using AWS Snowball to transfer large amounts of data to and from AWS using
physical devices
58. Using AWS Snowmobile to transfer large amounts of data to and from AWS using a
45-foot shipping container
59. Using Amazon GuardDuty to detect and respond to security threats and
vulnerabilities in AWS accounts and workloads
60. Using Amazon Detective to investigate and analyze security issues in AWS accounts
and workloads
61. Using AWS Glue DataBrew to prepare and clean data for analytics and machine
learning
62. Using Amazon Fraud Detector to detect and prevent fraud in applications and
services63. Using AWS AppConfig to deploy and manage application configurations across
multiple environments
64. Using AWS Chatbot to receive and respond to notifications from AWS services in
chat channels
65. Using AWS Cost Explorer to analyze and optimize AWS spending across accounts
and services
66. Using AWS Data Exchange to find, subscribe to, and use third-party data in AWS
workloads
67. Using Amazon Elasticsearch Service to search and analyze data in real-time
68. Using Amazon Honeycode to build custom applications without writing code
69. Using Amazon Location Service to add location-based features and analytics to
applications and services
70. Using Amazon Managed Service for Prometheus to monitor and troubleshoot
containerized applications on AWS
71. Using Amazon SageMaker Data Wrangler to prepare data for machine learning
models
72. Using AWS IoT Greengrass to run AWS services locally on IoT devices
73. Using AWS IoT SiteWise to collect and analyze industrial data for operational
insights
74. Using AWS IoT Things Graph to build and deploy IoT applications and workflows
75. Using Amazon Lookout for Metrics to detect anomalies in business metrics and KPIs
76. Using Amazon DevOps Guru to improve application availability and performance
using ML-powered insights
77. Using Amazon Elastic Kubernetes Service (EKS) to deploy, manage, and scale
containerized applications using Kubernetes on AWS
78. Using AWS Fargate to deploy and manage containers without managing the
underlying EC2 instances
79. Using AWS Lake Formation to build and manage secure data lakes in AWS
80. Using Amazon Managed Service for Grafana to visualize and monitor metrics for
applications and services on AWS
81. Using Amazon Managed Service for Prometheus to monitor and troubleshoot
containerized applications on AWS
82. Using Amazon Managed Service for Kafka to manage and scale Apache Kafka
clusters on AWS
83. Using AWS App Runner to automatically build and deploy containerized
applications on AWS
84. Using AWS Audit Manager to continuously audit and report on compliance
85. Using AWS Control Tower to set up and govern a multi-account AWS environment
86. Using AWS Glue to extract, transform, and load data from various sources into AWS
for analysis
87. Using Amazon Kinesis to collect, process, and analyze real-time streaming data
from various sources
88. Using AWS Lambda to run code in response to events and triggers without
provisioning or managing servers
89. Using Amazon MQ to manage message brokers for enterprise messaging workloads
90. Using AWS PrivateLink to access AWS services over a private connection without
going over the public internet
91. Using Amazon QuickSight to create interactive dashboards and reports for data
analysis
92. Using Amazon Redshift to store and analyze large amounts of data in a data
warehouse
93. Using AWS Resource Access Manager to share AWS resources across accounts and
organizations
94. Using Amazon S3 to store and retrieve data from anywhere on the web with high
scalability, durability, and security
95. Using Amazon SES to send email messages and manage email addresses for
applications and services
96. Using Amazon SNS to send and receive messages and notifications across
distributed systems and applications
97. Using Amazon SQS to decouple and scale microservices, distributed systems, and
serverless applications
98. Using AWS Storage Gateway to bridge on-premises and cloud storage for backup,
disaster recovery, and hybrid cloud scenarios
99. Using AWS Transfer Family to transfer files over SFTP, FTPS, and FTP using AWS
100. Using AWS WAF to protect web applications and APIs from common web exploits
and attacks.
