AWS services and their use cases

Core services in AWS:AWS services

  1. Compute in AWSCompute

    1. Amazon EC2(Elastic Compute Cloud):
      1. It provides dedicated virtual servers that have remote access. In addition, it enables you to pay only for what you use. You can also choose different types of instances based on your requirement in AWS.
    2. EC2 instance types
      1. Some of the instance types are:
        1. Memory-optimized –

          These are used to deliver fast performance for workloads that process large data sets in memory. Some of the use cases are:

            1. High-performance, relational (MySQL) and NoSQL (MongoDB, Cassandra) databases.
            1. Distributed web-scale cache stores that provide in-memory caching of key-value type data (Memcached and Redis).
            1. In-memory databases using optimized data storage formats and analytics for business intelligence (for example, SAP HANA).
            1. Applications performing real-time processing of big unstructured data (financial services, Hadoop/Spark clusters).
          1. High-performance computing (HPC) and Electronic Design Automation (EDA) applications.
        2. Compute-optimized,
        3. Storage and input-output optimized and
        4. GPU optimized or a generic instance etc.
      2. Pricing types:
        1. On-demand pricing for spiky workloads.
        2. Reserved pricing for steady state workflows with committed utilization.
        3. Spot pricing provides the best hourly rate, for workloads that are not time dependent.
    3. Amazon ECS(EC2 Container Service): Run on a managed cluster of EC2 intances.
    4. Apart from that, Amazon Lambda lets you run code without provisioning or managing servers. Hence, you can pay only for the compute time consumed. In addition, This can be used as an event-driven task computes service.
  2. Storage and Content delivery:

    1. EBS(Elastic Block Store) is similar to the regular hard drive(1 GB to 16 TB). Secondly, It comes in handy when there is a requirement for database, file system and raw block level storage. Moreover, it is primarily used with EC2.
    2. S3(Simple Storage Service):
      1. Firstly, It is used for static website content such as images and video.
      2. Secondly, in data stores for analytic applications like Hadoop clusters.
      3. In addition for Backups etc.
    3. Glacier is for Archiving and backup. In addition, you can automate the lifecycle process for Archival in terms of timelines and triggers to archive from S3 to Glacier.
    4. Snowball is used for large-scale data transfers from in and out of AWS.
    5. Amazon CloudFront:
      1. To make sure the content is getting downloaded from the nearest region for low latency and better customer experience, requests are automatically routed to nearest edge location.
  3. Database:

    1. AMAZON RDS:
      1. Firstly, It is a relational database in the cloud.
      2. Secondly, It provides six database engines to choose from. They are Amazon Aurora, PostgresSql, MySql, MariaDB, Oracle database, SQL server.
      3. In addition, you can choose a different database instance types for optimization of memory, performance, and IO.
      4. Apart from that, you can use AWS Data Migration Service to move from existing databases to RDS.
    2. Dynamo DB: NoSQL, supports both document and key-value store.
    3. AWS Data Migration Service:
    4. Redshift: Data warehousing and Analytics.
  4. Messaging:

    1. SQS:
      1. Firstly, It is a very helpful tool to communicate between different Microservices/components. For example, Let us say,  the first service is highly scaled and the second subsequent service that handles all those requests in the backend is not scaled on par with the first Micro service. If the two services communicate directly, you will get throttling or transient exceptions from the second service because of its low availability. you will lose some client-side requests. Suppose if we use SQS in between, SQS can consume all the incoming traffic and subsequent microservice and process them at its own pace and availability with loss of any requests. Even if the subsequent service is down, we will still have all the requests stored safely in SQS. Hence, SQS can take the load of mismatch between scaling issues of different interacting Microservices.
      2. Secondly, It has two types again,
        1. Standard Queue:
        2. FIFO Queue: It enables deduplication and ordering of messages.
  5. AWS Developer Tools:

    Developer tools

    1. Cloud9 is a cloud-based development environment that you can use to write, run and debug code using a web browser.
  6. Application Services:

    1. API Gateway
      1. Firstly, It allows you to create, manage and host a Restful API.
    2. SQS
      1. Firstly, It helps in transmitting messages between two components of your service.
  7. Enterprise Applications:

    1. Amazon workspaces:
  8. Analytics:

    1. Amazon EMR: EMR
      1. Firstly, It uses the Apache Hadoop framework and is hosted on EC2.
      2. Perform data-intensive tasks like:
        1. web indexing.
        2. data mining.
        3. log file analysis.
        4. financial analysis.
    2. Amazon Kinesis Data Firehose:KinesisKinesis 2
      1. Firstly, It is a platform for streaming data on AWS.
      2. Moreover, It provides the ability to build custom streaming data applications.
      3. In addition, It also automatically scales to match the data throughput.
  9. Networking:

    1. Amazon VPC(Virtual Private cloud):
    2. Direct Connect:
      1. Most importantly, It enables private high bandwidth connectivity between AWS and your data center.
    3. Route 53:
      1. Latency-based routing: Firstly, It routes traffic to the region with low latency.
      2. Weighted Round Robin: In addition, It specifies a proportion of traffic routed to each server.
      3. DNS Failover: Moreover, If the resource becomes unavailable, reroute traffic to an alternate location.
      4. Above all, Elastic Load Balancing is also integrated.
  10. Management Tools:

    1. CloudWatch:
      1. Most importantly, CPU Utilization, disk, and network traffic metrics are key targets of Cloudwatch.
    2. CloudFormation:
      1. Firstly, It enables you to deploy AWS resources programmatically with a simple JSON-formatted template.
      2. In addition, It allows you to quickly provision replicas of your production environment. Therefore, enables to conduct development and testing.
    3. Trusted Advisor:
      1. Most importantly, It provides real-time guidance to best manage AWS resources. Moreover, It covers cost-cutting, security, performance and Fault Tolerance.
  11. Machine Learning:Amazon ML stack

    1. Amazon SageMaker:
      1. Firstly, It comes with autoscaling and prebuilt containers. Secondly, It also removes the need for configuration and infrastructure management.

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