When managing EC2 instances in AWS, understanding the difference between stopping and terminating is essential. When an EC2 instance is stopped, it performs a regular shutdown. This means the operating system halts gracefully, allowing any ongoing transactions to finish properly. After the shutdown, the instance remains in AWS but is no longer active. The associated EBS volumes persist, allowing you to restart the instance later with the same data and configurations. During the stopped state, you are not billed for instance hours, though storage charges for the attached volumes still apply.
In contrast, terminating an instance is a permanent action. Once terminated, the instance is deleted entirely from AWS, and any associated EBS volumes are removed unless you specifically disable the “Delete on Termination” flag. A terminated instance cannot be restarted, and unless data is backed up to persistent storage such as Amazon S3 or retained volumes, it is lost forever. This operation is typically used when you no longer need the instance and want to avoid ongoing charges.
Understanding the lifecycle of an EC2 instance allows architects to make informed decisions on resource management, cost control, and availability strategies.
Setting Tenancy for Single-Tenant Hardware
Tenancy refers to how EC2 instances are placed on underlying hardware. By default, instances are launched on shared hardware. However, certain scenarios require that instances run on single-tenant hardware for reasons such as security, compliance, or performance. In such cases, you must configure the tenancy of the instance to “dedicated.”
Using a dedicated tenancy ensures that your instance is placed on physical servers that are not shared with any other AWS customers. This isolation can be important for workloads subject to regulatory requirements or specific licensing agreements. You can specify tenancy at the instance level or create a dedicated VPC where all launched instances use dedicated hardware.
Understanding tenancy is important when designing secure and compliant AWS environments. Misconfigured tenancy settings can result in increased costs or failure to meet organizational policies.
Elastic IPs and Cost Implications
Elastic IP (EIP) addresses are static public IPv4 addresses designed for dynamic cloud computing. They allow users to maintain a consistent public IP address even when instances are stopped, started, or replaced. However, improper use of Elastic IPs can lead to unnecessary costs.
You are not charged for one Elastic IP address associated with a running instance. However, AWS applies charges in the following situations: when an Elastic IP is associated with a stopped instance or when it is not associated with any instance. These charges encourage efficient use of the limited pool of public IPv4 addresses.
Elastic IPs are useful in failover scenarios, where you need to quickly remap the IP address to a standby instance in the event of a failure. They also help maintain DNS records or firewall rules that rely on fixed IPs. Nevertheless, architects should monitor usage and release unused IPs to avoid waste and ensure cost optimization.
Spot Instances vs On-Demand Instances
AWS offers several pricing models for EC2 instances, each suitable for different workload requirements. Spot Instances and On-Demand Instances are commonly used but serve distinct purposes.
On-Demand Instances provide predictable pricing and can be launched at any time without commitment. They are ideal for short-term, irregular workloads that cannot tolerate interruptions. Users pay only for the hours or seconds used, depending on the instance type. This flexibility comes at a higher cost compared to other models.
Spot Instances allow users to bid on unused EC2 capacity. They offer significant cost savings, often up to 90% compared to On-Demand pricing. However, AWS can terminate Spot Instances at short notice when capacity is required for other services. Because of this, Spot Instances are best suited for stateless, fault-tolerant workloads like batch processing, big data analysis, or CI/CD pipelines.
An optimal architecture may combine both types. For example, On-Demand Instances can handle critical workloads, while Spot Instances are used for background or auxiliary tasks. This blend achieves a balance between reliability and cost-efficiency.
Availability of Multi-AZ Deployments Across Instance Types
Multi-AZ deployments are a common strategy for ensuring high availability and fault tolerance in AWS environments. While the term is often associated with managed services like Amazon RDS, the concept also applies to EC2 instances.
Multi-AZ deployment involves placing EC2 instances across different Availability Zones within a region. This geographical distribution protects applications against data center-level failures. If one zone becomes unavailable, the application continues to function using resources in another zone.
While all EC2 instance types can be used in a Multi-AZ setup, proper configuration is required. This includes setting up load balancers, auto-scaling groups, and possibly DNS routing policies. The architecture must be designed to ensure that traffic is rerouted in the event of failure and that stateful components are replicated as needed.
Using Multi-AZ deployments effectively improves system resilience and supports disaster recovery objectives.
Network Performance in Cluster Placement Groups
Cluster Placement Groups are used to achieve high network performance among EC2 instances. When you launch instances in a Cluster Placement Group, AWS places them close together within the same hardware infrastructure in a single Availability Zone.
This proximity enables low-latency, high-throughput communication, which is essential for certain workloads such as High-Performance Computing (HPC), scientific simulations, or distributed analytics. Specific network performance expectations include:
- Up to 20 Gbps in full-duplex mode or multi-flow scenarios
- Up to 10 Gbps in single-flow scenarios
- External traffic to other groups or zones is typically capped at 5 Gbps
The network performance depends heavily on the instance type and whether it supports enhanced networking. Not all instance families support placement groups, so compatibility should be verified during planning.
Careful capacity management is also essential, as AWS may not be able to fulfill placement requests if the zone lacks suitable capacity. Launching all required instances at once increases the likelihood of success.
Deploying a Hadoop Cluster Using EC2 Instances
Amazon EC2 provides flexibility for deploying custom Hadoop clusters tailored to specific workloads. When setting up a 4-node Hadoop cluster, instance selection is a critical consideration. Options like i2.large offer high IOPS, while c4.8xlarge delivers strong compute performance. The choice depends on whether the workload is I/O-bound or CPU-bound.
Alternatively, AWS offers Amazon EMR (Elastic MapReduce), a managed service that simplifies the deployment and management of Hadoop clusters. With EMR, you can upload data to S3, process it using a managed Hadoop cluster, and then store the output back in S3. This approach reduces operational complexity and enables easy scaling.
EMR supports various EC2 instance types, and you can mix Spot and On-Demand instances for cost savings. Additionally, EMR handles software patching, provisioning, and cluster configuration automatically, freeing up engineers to focus on data processing tasks.
Understanding when to use EC2-based Hadoop and when to leverage EMR is key for architects planning big data solutions in AWS.
Overview of Amazon Machine Images (AMIs)
An Amazon Machine Image (AMI) is a blueprint for launching EC2 instances. It includes the operating system, application server, and any applications or libraries configured by the user. AWS provides a wide variety of AMIs for different use cases, ranging from general-purpose OS images to images with pre-installed software stacks.
Custom AMIs can be created to capture the state of a configured instance. This is useful for replicating environments, creating backups, or maintaining consistent application deployments. For example, a development team might create a custom AMI with pre-installed development tools and libraries, allowing team members to quickly spin up identical environments.
Not all AMIs are free. Some are provided by third-party vendors and come with associated costs. AMIs can also be shared across accounts or made public. Custom AMIs help reduce launch times and ensure consistency across environments.
Maintaining a versioned catalog of AMIs ensures traceability and aids in rollback strategies during deployment.
Factors to Consider When Choosing Availability Zones
Choosing the right Availability Zone (AZ) for your workloads involves more than random selection. Several key factors must be evaluated, including:
- Latency: Applications requiring real-time response must be deployed in zones geographically close to the end users.
- Cost: While prices are generally consistent within a region, some services may incur different charges depending on the zone.
- Performance: Resource availability, such as specialized instance types or newer hardware generations, may vary between zones.
- Compliance: Certain workloads may need to be deployed in zones that meet specific regulatory or legal requirements.
- Redundancy: Spreading resources across multiple zones ensures that failures in one zone don’t bring down the entire application.
Balancing these factors helps achieve high availability, better user experience, and optimized cost management.
Public and Private Addressing in EC2 Instances
When an EC2 instance is launched, AWS assigns it both a private and a public IP address (unless otherwise configured). The private IP address is used for internal communication within the VPC. This address remains associated with the instance during its lifecycle and facilitates communication with other instances in the same network.
The public IP address enables external access to the instance over the internet. It is assigned dynamically and changes when the instance is stopped and restarted unless an Elastic IP is used. Public IPs are critical for hosting services that need to be accessible from outside the VPC.
Elastic IPs solve the problem of dynamic reassignment by providing a persistent public IP. These can be reassigned quickly across instances in failover scenarios.
For production environments, relying on Elastic IPs and managing DNS configurations appropriately ensures reliability and consistent connectivity.
Hosting Multiple Websites on a Single Elastic IP
In general, each Elastic IP address in AWS is intended to be associated with one EC2 instance. This poses a limitation when attempting to host multiple websites that each require their public-facing endpoint. While technically it is possible to serve multiple websites from a single IP address using techniques such as name-based virtual hosting within a web server like Apache or Nginx, there are caveats.
These techniques work effectively if all the domains share the same IP and differ only by the host header. However, for SSL/TLS-enabled sites, managing multiple certificates on one IP becomes more complex, especially without Server Name Indication (SNI) support. Moreover, from an AWS perspective, only one Elastic IP can be associated with an instance’s primary network interface at a time. Hosting multiple SSL-enabled domains without SNI requires multiple IPs, which means multiple Elastic IPs.
To support multiple Elastic IPs, an instance must have multiple network interfaces or multiple secondary private IPs that Elastic IPs can map to. This setup is more complex and typically only warranted in specific use cases, such as multi-tenant applications or hosting legacy applications with rigid IP requirements.
Security Best Practices for Amazon EC2
Securing EC2 instances involves multiple layers of defense and continuous vigilance. Security Groups act as virtual firewalls, and they should be reviewed regularly to ensure only required ports and protocols are allowed. Implementing the principle of least privilege means granting minimal necessary access to users, processes, and services.
Using IAM roles for instances is preferred over embedding credentials into applications. Password-based login should be disabled in favor of SSH key-based authentication. Disabling root login and enforcing multi-factor authentication for administrative accounts adds further security.
Auditing and logging activity through services like AWS CloudTrail and Amazon CloudWatch helps in detecting unauthorized or unusual actions. Enabling automatic software updates and vulnerability scanning keeps the environment compliant with security policies. Isolating instances using VPC subnets and network ACLs strengthens network segmentation, reducing the attack surface.
A comprehensive security policy combined with automation tools like AWS Config or AWS Inspector helps enforce compliance and respond to threats more efficiently.
Understanding Processor State Control in EC2
Processor state control allows fine-tuning of how EC2 instances consume CPU power. It includes two major mechanisms: P-states and C-states. These are part of the ACPI power management specification used to balance performance with energy efficiency.
P-states (performance states) range from P0 to P15, with P0 indicating maximum frequency and performance, and P15 representing the lowest. Adjusting P-states helps reduce power usage during low workloads. However, frequent transitions between states may impact latency-sensitive applications.
C-states (idle states) determine the level of processor sleep when idle. C0 means the processor is fully active, while higher C-states, such as C6, allow deeper sleep, reducing power draw further. Transitioning between deeper C-states and back to C0 can introduce latency.
AWS provides options to manage these states for specific EC2 families where fine-grained control is necessary, such as compute-optimized instances used in HPC or low-latency applications. Understanding and configuring processor state behavior is essential when optimizing for workload characteristics.
Restricting Third-Party Access to S3 Buckets
Managing access to Amazon S3 is critical for maintaining data confidentiality and compliance. One common scenario involves restricting third-party applications or users from accessing a particular bucket, such as one used for backups. This is achieved using IAM policies that define who can perform specific actions on resources.
A custom IAM user policy is typically crafted to allow or deny access to S3 buckets based on criteria like bucket name, actions (such as GetObject, PutObject), and resource ARN. The policy can deny all access by default and allow only specified principals and actions. Additionally, bucket policies can be applied directly to control access at the bucket level.
For third-party integrations, granting temporary credentials using AWS STS and assigning limited permissions via roles is recommended. This avoids long-term exposure and ensures tight control over what external services can access. Logging all access via S3 server access logs or AWS CloudTrail helps audit and investigate access patterns.
Integrating S3 with EC2 Instances
Amazon S3 is a highly available and durable storage service, while EC2 provides scalable compute. Integration between the two allows flexible and powerful architectures. EC2 instances can access S3 to fetch data, configuration files, or code, and also store logs, backups, or processed outputs.
This integration is commonly done using AWS SDKs or CLI tools installed on the EC2 instance. IAM roles are attached to the instance to grant the necessary permissions. The AWS S3 cp or sync commands are frequently used for data transfer. Developers often configure S3 endpoints in applications running on EC2 to stream or process data.
In certain use cases, such as web hosting or machine learning, large datasets are kept in S3 while EC2 handles computation. This separation allows independent scaling of storage and compute. If the EC2 instance has a root EBS volume, it does not interfere with accessing S3, as S3 operates independently of EC2 block storage.
Efficient integration involves managing permissions carefully, using S3 lifecycle policies to reduce costs, and securing communication with encryption and signed URLs when necessary.
Improving Data Transfer with Snowball
AWS Snowball is a data transport solution designed for large-scale transfers to and from AWS. It bypasses network bottlenecks by shipping a secure appliance that customers load data into before returning it to AWS. The appliance uses strong encryption and tamper-resistant mechanisms to protect data during transit.
To improve the speed of data transfer into Snowball, users can employ a few strategies. One method is to parallelize the data copy process by using multiple host machines to write data simultaneously. This distributes the workload and speeds up the transfer.
Another technique is splitting large datasets into many smaller files. Large files are more susceptible to encryption overhead and latency, whereas smaller files allow parallel encryption and faster writing to disk. Repeating copy operations concurrently, especially when the source system is robust, can also improve throughput.
Snowball also supports clustering multiple devices for petabyte-scale migrations. Once returned to AWS, the data is ingested into services like S3 or Glacier, depending on the use case. Snowball Edge variants even allow local data processing before transfer, further enhancing efficiency.
Transferring Data Over Long Distances
Data transfer over long geographic distances can suffer from latency and inconsistent throughput. AWS offers solutions designed to accelerate this process, with Amazon S3 Transfer Acceleration being the most notable.
Transfer Acceleration works by routing traffic through AWS edge locations and using optimized network paths within the AWS global backbone. This significantly reduces latency and packet loss, especially when transferring data across continents. Users simply enable the feature on an S3 bucket and use the specially formatted endpoint.
For extremely large datasets or when network capacity is insufficient, AWS Snowball is an alternative, but it is not ideal for ongoing data replication or low-latency operations. Transfer Acceleration remains the best option for fast, cross-regional uploads and downloads.
For enterprise workloads like real-time backups or remote application data synchronization, combining Transfer Acceleration with multi-region S3 replication ensures both speed and resilience.
Launching Instances in a Virtual Private Cloud
A Virtual Private Cloud (VPC) is AWS’s logically isolated section of the cloud where users define and control network configurations. Launching EC2 instances within a VPC is standard practice and enables fine-grained control over IP addressing, routing, subnets, and security.
Each EC2 instance in a VPC receives a private IP address from the subnet’s CIDR block. If public access is needed, an Elastic IP or public IP is assigned. Instances within the VPC can communicate with each other using private IPs, and connectivity to on-premises data centers or the internet is controlled through route tables, internet gateways, and NAT gateways.
Using VPCs allows for the creation of multiple subnets—public for web servers and private for databases. Security groups and network ACLs further refine access control. Launching instances in VPCs is essential for implementing network segmentation, enforcing compliance, and optimizing security posture.
VPCs also support flow logs, VPC peering, and gateway endpoints, enabling powerful architectures that span cloud and on-prem environments.
Connecting AWS to Corporate Data Centers
AWS enables hybrid architectures through seamless connectivity between the cloud and on-premises environments. One primary method is using a Virtual Private Network (VPN) connection. This establishes a secure IPSec tunnel between the corporate data center and the AWS VPC.
Setting up a VPN involves configuring a virtual private gateway in AWS and a customer gateway in the data center. Static or dynamic routing can be used, depending on the architecture. VPN connections offer encryption and reasonable performance for moderate workloads.
For higher bandwidth and consistent performance, AWS Direct Connect is another option. It provides a dedicated physical link between the data center and AWS, bypassing the public internet. This is suitable for latency-sensitive applications, large data transfers, or compliance-driven deployments.
In hybrid environments, tools like AWS Storage Gateway and AWS Directory Service enable seamless resource sharing and identity federation. Establishing connectivity is foundational to migrating workloads, extending existing infrastructure, or implementing disaster recovery strategies.
Immutability of EC2 Private IP Addresses
An EC2 instance’s private IP address is tied to its lifecycle. Once assigned at launch, the private IP cannot be changed while the instance is running. This immutability ensures consistent internal communication and simplifies network configuration.
Private IPs remain with the instance until it is stopped or terminated. When restarted, the same private IP is usually reassigned unless manually released. However, AWS allows assigning secondary private IPs that can be reconfigured dynamically while the instance is running. This flexibility is useful in high-availability or failover scenarios where secondary IPs are moved between instances.
If a different private IP is needed, the instance must be stopped and restarted with a new network configuration. Managing these addresses effectively ensures that dependent systems continue to operate correctly without requiring reconfiguration.
Placement Groups in EC2
Placement groups in Amazon EC2 are logical groupings of instances designed to meet specific workload requirements related to networking, latency, or high availability. There are three types of placement groups:
- Cluster: All instances are placed within a single Availability Zone and are connected via low-latency, high-throughput networking. This is suitable for HPC (high-performance computing), big data, or tightly coupled applications.
- Spread: Instances are distributed across different underlying hardware to reduce correlated hardware failures. This is ideal for small numbers of critical instances that must be kept isolated from one another.
- Partition: Instances are divided into logical partitions, each isolated from others. It’s commonly used for large-scale, distributed systems like Hadoop or Kafka, where fault tolerance is crucial.
Choosing the right type of placement group depends on the workload’s tolerance for fault isolation and the need for network performance. Once an instance is launched in a placement group, its placement cannot be changed; instead, the instance must be stopped and relaunched into a new group.
Benefits of Elastic Load Balancing
Elastic Load Balancing (ELB) automatically distributes incoming traffic across multiple targets, such as EC2 instances, containers, and IP addresses. It helps achieve high availability, fault tolerance, and scalability.
ELB supports different types of load balancers:
- Application Load Balancer (ALB): Best for HTTP/HTTPS and WebSocket traffic. It offers routing based on request paths, host headers, or methods.
- Network Load Balancer (NLB): Designed for ultra-low latency and high throughput, useful for TCP/UDP-based applications.
- Gateway Load Balancer (GWLB): Enables the deployment of third-party virtual appliances in a scalable manner.
ELB improves application fault tolerance by rerouting traffic from unhealthy to healthy targets and performs health checks automatically. It also integrates with Auto Scaling to dynamically add or remove instances behind the load balancer as traffic fluctuates.
By terminating SSL at the load balancer, ELB offloads encryption processing, improving backend performance. It also supports stickiness, WebSocket support, and native integration with AWS services like WAF for added security.
Amazon CloudWatch Overview
Amazon CloudWatch is a monitoring and observability service for AWS cloud resources and applications. It provides real-time metrics, logs, alarms, and dashboards to help developers and operators understand system performance and respond to operational issues.
CloudWatch collects metrics from AWS services like EC2, RDS, Lambda, and custom applications. These metrics can be visualized on customizable dashboards. It also supports setting alarms to trigger notifications or actions, such as scaling or restarting services, when a threshold is breached.
CloudWatch Logs aggregates logs from multiple sources, enabling centralized logging, log retention policies, and real-time search and analysis. It integrates with CloudWatch Insights for advanced query capabilities.
CloudWatch Events (now EventBridge) allows event-driven automation based on system state changes, like instance start/stop. Combined with CloudWatch Agent, it extends monitoring to system-level metrics such as disk I/O, memory, and processes.
CloudWatch helps optimize resource utilization, reduce costs, and enhance system reliability through proactive monitoring and alerts.
Default Storage for EC2 Root Volumes
When launching an EC2 instance, the root volume serves as the operating system disk. By default, this root volume is backed by Amazon Elastic Block Store (EBS), which provides persistent, block-level storage.
The EBS root volume can be configured to remain after instance termination (using the DeleteOnTermination flag) or be automatically deleted. EBS volumes are durable and can be backed up using snapshots, allowing recovery and cloning.
For some instance types, instance store (also called ephemeral storage) can be used as the root device. This storage is physically attached to the host and offers high IOPS, but is non-persistent—data is lost when the instance is stopped or terminated.
EBS root volumes support encryption, resizing, and performance tuning (via volume types such as gp3, io2, etc.), providing flexibility for different workloads.
Understanding Amazon Machine Images (AMI)
An Amazon Machine Image (AMI) is a preconfigured template that contains everything needed to launch an EC2 instance, including:
- Operating system
- Application server
- Custom software
- EBS snapshot (for root volume)
Users can select AMIs provided by AWS, AWS Marketplace, or create their own. Custom AMIs are especially useful for creating golden images that include baseline configurations, software, and security patches for consistent deployment.
AMI types are region-specific and can be copied across regions if needed. They support both EBS and instance store-backed root volumes. AMIs are integral to automated deployments, allowing faster scaling and predictable environments.
Creating an AMI from an existing EC2 instance ensures that its configuration can be replicated in the future. This is often used in Auto Scaling groups and launch templates for infrastructure-as-code setups.
Stopping and Terminating EC2 Instances
Stopping an EC2 instance is like shutting down a computer. The instance’s EBS volumes remain intact, and its configuration and metadata are preserved. When restarted, the same instance ID is retained, but the public IP may change unless it’s an Elastic IP.
Terminating an instance is permanent. The instance is deleted, and any EBS volumes with DeleteOnTermination enabled are also deleted. The instance ID cannot be reused. This action is typically taken when an instance is no longer needed.
Stopping is useful for pausing workloads to save costs while retaining the configuration. Terminating is used when permanently decommissioning instances. It’s important to back up data before termination, especially if the volume is not configured for persistent storage.
CloudWatch alarms and automation can trigger stop or terminate actions to manage costs or enforce operational policies.
Types of Load Balancers in AWS
AWS offers several types of load balancers through the Elastic Load Balancing (ELB) service, each optimized for different use cases:
- Application Load Balancer (ALB): Layer 7, operates at the request level. Supports advanced routing features like host-based, path-based, and header-based routing. Ideal for microservices and modern web applications.
- Network Load Balancer (NLB): Layer 4, designed for high-performance TCP and UDP traffic. Supports millions of requests per second with ultra-low latency. Suitable for performance-critical applications.
- Gateway Load Balancer (GWLB): Provides traffic distribution combined with third-party appliance integration, such as firewalls or intrusion detection systems. It supports the GENEVE protocol for encapsulated traffic inspection.
Each load balancer type supports automatic scaling, health checks, and integration with Auto Scaling groups. They also offer SSL termination and logging features, making them suitable for both simple and enterprise-level architectures.
Using Roles in AWS
Roles in AWS Identity and Access Management (IAM) define a set of permissions that can be assumed by entities like users, applications, or services. Unlike IAM users, roles are not tied to a specific identity but are assumed as needed.
Roles enable secure, temporary access to AWS resources. They are commonly used in:
- Cross-account access
- Federated authentication
- Assigning permissions to EC2 instances
- Granting access to AWS services like Lambda or ECS
For example, instead of embedding credentials in an EC2 instance, an IAM role can be attached at launch time, giving the instance secure access to S3, DynamoDB, or other services.
Roles use trust policies to define who can assume them, and permission policies to define what actions are allowed. Temporary credentials issued via AWS STS make them ideal for short-term use cases.
Using roles reduces the risk of credential leakage and supports best practices for secure, scalable access management.
Connecting Multiple EC2 Instances to a Single Volume
Amazon EBS volumes are designed to be attached to a single EC2 instance at a time. Attaching one volume to multiple instances concurrently is not supported unless using a specialized configuration.
For shared storage, AWS provides alternatives:
- Amazon EFS (Elastic File System): Supports simultaneous connections from multiple EC2 instances, similar to a traditional NFS share.
- Amazon FSx for Lustre or Windows File Server: Offers high-performance or Windows-compatible shared file systems.
- EBS Multi-Attach: Available on select io1/io2 volumes, allowing up to 16 Nitro-based EC2 instances to attach to the same volume. This requires cluster-aware file systems like Oracle ASM or GFS2 to manage concurrency and prevent data corruption.
In general, shared access requires a file system designed to handle concurrent writes and reads, making EFS or FSx better suited for typical applications needing shared volumes.
Connecting a Corporate Data Center to the AWS Cloud
Establishing a secure connection between a corporate data center and AWS is often a critical part of hybrid cloud strategies. There are several options for achieving this connectivity depending on performance, security, and cost requirements.
One method is to use a Virtual Private Network (VPN). AWS provides support for creating IPsec VPN tunnels that connect a Virtual Private Cloud (VPC) to on-premises infrastructure. These connections run over the public internet and are suitable for many applications, though they can be subject to latency and bandwidth variability.
Another, more robust option is AWS Direct Connect, which provides a dedicated network connection between the data center and AWS. This method bypasses the internet entirely, reducing latency and providing consistent network performance. It is often used by enterprises that transfer large volumes of data or require low-latency access to AWS services.
Hybrid connections may also use AWS Transit Gateway to manage multiple VPCs and on-premises networks, simplifying connectivity and routing.
Both VPN and Direct Connect support encryption and failover mechanisms, ensuring secure and reliable communication between environments.
Private and Public IP Addresses in EC2
EC2 instances are assigned both private and public IP addresses to facilitate different types of communication.
Private IP addresses are used for internal communication within the AWS network and VPC. These addresses remain the same across instance stops and starts and are not reachable from the public internet. They are critical for communication between instances and other AWS resources within a secure boundary.
Public IP addresses, on the other hand, allow instances to be accessed over the internet. These are dynamically assigned and may change when an instance is stopped and restarted, unless an Elastic IP address is used. Elastic IPs are static, public IPv4 addresses that can be associated with any instance within a region and re-associated as needed.
It is best practice to use private IPs for internal services and reserve public or Elastic IPs for applications requiring external access. This approach reduces exposure to the public internet and enhances security.
Elastic IP Addresses and Cost Considerations
Elastic IP addresses (EIPs) are static public IPv4 addresses designed for dynamic cloud computing. While they are free when associated with a running instance, AWS charges for them when they are not in active use.
Charges apply under the following conditions:
- The EIP is not associated with any instance.
- The EIP is associated with a stopped instance.
- More than one EIP is associated with a single instance.
The rationale behind this pricing model is to encourage efficient use of limited IPv4 resources. To avoid unnecessary costs, users should release any EIPs not in use and consider using DNS or Application Load Balancers for high availability instead of relying solely on static IPs.
Elastic IPs are valuable for use cases where public IP stability is essential, such as for failover configurations or long-lived services.
Access Control in Amazon S3
Amazon S3 provides multiple layers of access control to manage permissions for buckets and objects. These include:
- Bucket policies: JSON-based policies attached to buckets that define access rules. These policies can allow or deny access based on IP address, user, AWS account, and more.
- IAM policies: Grant permissions to AWS users, groups, and roles to access S3 resources. These are more granular and are often used in combination with bucket policies.
- Access Control Lists (ACLs): A Legacy feature allowing permission management at the individual object level. Less commonly used due to limitations in granularity and clarity.
- S3 Block Public Access: A global setting to prevent public access to buckets and objects, regardless of bucket policy or ACL settings. It helps prevent accidental exposure of data.
In addition to these, S3 Access Points can be configured to simplify managing access to shared data sets for different applications or teams.
Security best practices include:
- Enabling server-side encryption (SSE)
- Using S3 versioning and MFA delete
- Logging and monitoring access with CloudTrail and S3 Access Logs
Monitoring with AWS CloudTrail
AWS CloudTrail is a service that enables governance, compliance, and operational auditing by recording account activity. It captures all API calls made to AWS services from the AWS Management Console, SDKs, command line tools, and other services.
Each event recorded by CloudTrail contains details such as the identity of the API caller, time of the API call, source IP address, and request parameters.
CloudTrail supports two types of trails:
- Management Events: Track actions like launching EC2 instances or changing security groups.
- Data Events: Capture operations on resources such as S3 object-level access or Lambda function invocations.
Logs can be stored in S3, analyzed using Athena, or sent to CloudWatch Logs for real-time alerts. CloudTrail integrates with AWS Organizations, allowing centralized auditing across multiple accounts.
Using CloudTrail helps ensure accountability, detect suspicious activity, and meet compliance requirements through detailed, tamper-proof logs.
Improving Snowball Transfer Speed
AWS Snowball is a physical appliance used to transfer large volumes of data into or out of AWS. There are several techniques to speed up data transfer to Snowball:
- Parallelization: Copy data using multiple threads or processes in parallel across different directories or sources. This ensures full utilization of available network and disk throughput.
- Smaller Files: Breaking up large files into smaller parts can enhance throughput and reduce encryption overhead, especially when using multi-threaded copy tools.
- Compression: Compress data before copying, especially when dealing with text files or data with low entropy. This reduces the volume of data transferred.
- Direct-to-Snowball Tools: Use the Snowball client or AWS OpsHub, which are optimized for the appliance and ensure efficient, encrypted data transfers.
Care should be taken to test transfer methods ahead of time and monitor performance metrics like write speed and queue depth during operation.
Use of Amazon Transfer Acceleration
Amazon S3 Transfer Acceleration is a feature designed to speed up uploads and downloads to and from S3 by using optimized Amazon CloudFront edge locations. It is particularly useful for applications with users located far from the S3 bucket’s region.
Transfer Acceleration works by routing data over Amazon’s high-speed network infrastructure, reducing the number of internet hops and congestion points. This is especially effective for large objects or users in remote geographical locations.
To enable Transfer Acceleration, it must be turned on for a specific bucket. Once enabled, users can use a specially formatted endpoint to take advantage of the acceleration.
Performance gains depend on the user’s distance from the bucket, and AWS provides a speed comparison tool to evaluate the benefit for specific locations.
AWS Direct Connect Backup Planning
AWS Direct Connect provides a dedicated network connection to AWS but requires redundancy planning. Without backup, connectivity failures can disrupt access to critical resources.
To ensure high availability:
- Use a backup VPN connection: Set up a VPN over the internet to serve as a failover option if Direct Connect fails.
- Enable BFD (Bidirectional Forwarding Detection): Provides rapid detection of failures and automatic routing to backup paths.
- Use multiple Direct Connect links: Provision connections in separate locations or availability zones to increase fault tolerance.
AWS recommends designing network architectures with at least one redundant path to avoid single points of failure. Using AWS Transit Gateway can further simplify and centralize these failover configurations.
Using Route Tables for Subnet Communication
In AWS, route tables control traffic routing within a VPC. Every subnet must be associated with a route table that defines how packets are directed.
A single route table can be associated with multiple subnets, allowing centralized control of traffic policies. However, each subnet can only be associated with one route table at a time.
Custom route tables allow adding routes to internet gateways, NAT gateways, VPN connections, or peering connections. This makes them essential for designing complex VPC topologies such as hybrid architectures or multi-tier applications.
Proper configuration of route tables ensures connectivity between instances, access to the internet, and secure communication with external networks.
Managing Multiple Websites on EC2 with Elastic IPs
Hosting multiple websites on a single EC2 instance using one Elastic IP is possible by configuring virtual hosts within a web server like Apache or Nginx. This setup uses the same IP but distinguishes sites by their domain name.
However, if separate SSL certificates, IP-level isolation, or load balancing are required, additional Elastic IPs or Application Load Balancers may be necessary.
Using a reverse proxy server or container orchestration (like ECS or Kubernetes) can help host multiple services more efficiently while managing traffic through a single endpoint.
Elastic IPs are a limited resource, so AWS encourages designs that use DNS and load balancers over multiple IP addresses for scalability and sustainability.
Final Thoughts
Preparing for an AWS Architect interview requires more than just technical know-how; it demands a solid understanding of cloud architecture principles, hands-on experience, and a mindset for solving real-world business problems using AWS services.
Amazon Web Services is an ever-evolving platform, and its architectural best practices are shaped by scalability, cost-efficiency, high availability, and security. The topics discussed across these parts—from EC2 behaviors to data transfer solutions and from IAM policies to VPC designs—represent not only common interview questions but also core responsibilities you may face in the role.
To make your preparation truly effective, consider the following insights:
Hands-on practice is irreplaceable
Conceptual knowledge is valuable, but being able to apply it through the AWS Management Console, CLI, or infrastructure-as-code tools like CloudFormation or Terraform brings deeper confidence and demonstrates real readiness.
Learn AWS Well-Architected Framework
This framework outlines the five pillars of cloud architecture: operational excellence, security, reliability, performance efficiency, and cost optimization. Understanding these principles will help you answer scenario-based questions with greater insight and business alignment.
Focus on integration and real-world use cases.
An architect’s job isn’t to memorize service specs, but to design systems that meet customer needs. Be ready to explain how to integrate services, manage trade-offs, and adapt to evolving requirements.
Stay up to date
AWS regularly introduces new services and features. Keeping up with announcements and regularly reading technical blogs or documentation helps you speak fluently about modern cloud strategies.
Be ready for system design questions.
Many interviews include design scenarios like “How would you build a multi-region, highly available web application?” These questions test your ability to balance trade-offs and apply architectural patterns to solve problems.
By mastering the foundational services, practicing real-world deployments, and understanding AWS design patterns, you’ll be well-prepared for your AWS Architect interview. Ultimately, the goal isn’t just to pass the interview, but to prove that you can build, manage, and optimize scalable cloud systems in a fast-paced environment.
If you’d like, I can help you build a custom mock interview or quiz based on these topics—or generate a printable PDF study guide with all the parts included. Just let me know.