Basic AWS Architecture Best Practices - 1 Hour Crash Course

In this lesson, we explore scalability in AWS cloud best practices, focusing on both vertical and horizontal scaling. Horizontal scaling, achieved by adding multiple instances behind an Elastic Load Balancer and using auto-scaling groups, is ideal for handling traffic efficiently, while the importance of stateless applications and high-performance computing (HPC) models, such as clusters and grids, is also emphasized for ensuring scalability and availability.

In this lesson, we explore the AWS best practice of using disposable resources, particularly focusing on making compute resources like EC2 instances temporary and easily replaceable. Auto scaling groups, along with Elastic Load Balancers (ELBs), automatically launch and terminate instances based on demand, ensuring that failed instances are replaced without manual intervention, embodying the concept of disposability.

In this lesson, we explore AWS automation, starting with auto-scaling EC2 instances based on pre-configured AMIs and user data scripts to ensure dynamic scaling and availability across multiple availability zones. Additionally, Lambda functions are highlighted as powerful tools for automating tasks like transcoding files in S3 buckets, allowing for seamless, event-driven processes without manual intervention.

This lesson explains the AWS best practice of loose coupling in application design, which allows different components of an application to work independently without direct dependencies on one another. By using tools like Elastic Load Balancers (ELBs) and Amazon SQS, application components can scale and fail independently, ensuring more flexible, resilient, and scalable systems.

This lesson explains the AWS architectural principle of using managed services and serverless technologies instead of managing individual servers. By leveraging services like AWS Lambda for code execution and S3 for hosting static websites, users can avoid the complexities of server management, while also benefiting from auto-scaling and paying only for what they use.

In this lesson, we explore how to design AWS infrastructure to avoid single points of failure by leveraging multi-availability zone (AZ) and multi-region architectures. By using components like Elastic Load Balancers, Multi-AZ RDS databases, and DynamoDB global tables, along with Route 53's failover capabilities, we ensure high availability and resilience even in the event of an AZ or entire region failure.

In this lesson, we explore how to optimize AWS resources for cost by utilizing the correct instance sizes and pricing models. By right-sizing instances, switching to reserved or spot instances, and considering long-term commitments, users can significantly reduce costs—sometimes by as much as 90%, depending on their workload and instance choices.

In this lesson, we explore the variety of database services available on AWS, emphasizing the flexibility to choose different database technologies based on specific use cases. AWS offers managed solutions such as RDS, DynamoDB, and Redshift, allowing users to easily deploy specialized databases tailored to their needs, without being confined to a single database type.

In this lesson, we explore how AWS caching services like CloudFront and ElastiCache can improve performance and reduce costs. CloudFront caches content at edge locations close to users, reducing load on the origin servers, while ElastiCache creates an in-memory cache for frequently accessed database data, enhancing read speeds and reducing the resource demands on databases such as RDS.

In this lesson, we explore AWS security best practices, focusing on the shared responsibility model where AWS secures the infrastructure, and customers are responsible for securing their resources. We also discuss defense-in-depth strategies, using multiple layers of security such as WAF, security groups, and network ACLs, along with best practices like temporary credentials and IAM roles to ensure secure interactions with AWS services.

In this lesson, we explore AWS Relational Database Service (RDS), a managed service that simplifies database management by automating tasks like backups, patching, and scaling. RDS supports multiple database engines like MySQL, PostgreSQL, Oracle, and Microsoft SQL, and offers different instance types with flexible pricing options, including on-demand and reserved instances for cost optimization.

In this lesson, we explore Amazon Aurora, a highly optimized relational database service exclusive to AWS that supports MySQL and PostgreSQL. Aurora offers superior performance, automatic failover to replicas, up to 15 read replicas with auto-scaling, and a unique cluster volume architecture that spreads data across three availability zones, ensuring high availability and fast, seamless replica creation.

In this lesson, we explore Amazon DynamoDB, a fully managed NoSQL database designed to handle massive, schema-less datasets with consistent performance at any scale. DynamoDB offers fast, SSD-based storage across three facilities, supporting both eventually consistent and strongly consistent reads, and is priced based on read and write capacity units rather than instance types or storage limits.