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Quick Definition

AWS stands for Amazon Web Services, the most widely adopted cloud provider offering on-demand compute, storage, networking, database, analytics, machine learning, security, and developer services.

Analogy: AWS is like a global utility grid for IT—rather than owning generators, transformers, and wires, you rent prebuilt capacity and services and pay for what you use.

Formal technical line: AWS is a distributed set of regionally isolated, multi-tenant cloud services delivered via APIs and managed control planes, underpinning infrastructure-as-a-service, platform-as-a-service, and managed application services.

If AWS has multiple meanings, the most common meaning is Amazon Web Services, the cloud provider. Other less common meanings:

• Amazon Web Services — legacy internal naming or shorthand.
• Not publicly stated
• Varied / depends

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What is AWS?

What it is / what it is NOT

• What it is: A comprehensive public cloud platform providing compute, storage, networking, databases, analytics, ML, identity, and management services with global regions and availability zones.
• What it is NOT: A single product, a private datacenter replacement in all cases, or a managed hosting provider with identical SLAs and operational models across every service.

Key properties and constraints

• Global-region model: Regions with Availability Zones (AZs) for data locality and fault isolation.
• API-driven: Nearly every capability exposed via REST or SDK.
• Shared responsibility: Security responsibilities divided between AWS and customers.
• Multi-tenancy and noisy neighbor mitigation: Logical isolation with shared physical resources.
• Variety of service levels: Highly managed (SaaS/PaaS) to raw virtualized infrastructure (IaaS).
• Cost complexity: Granular pricing that can be optimized or mismanaged.
• Rate limits and quotas: Soft and hard resource limits; quota increases possible.
• Configuration consistency: Misconfigurations are frequent root causes of incidents.

Where it fits in modern cloud/SRE workflows

• Platform foundation for applications, CI/CD pipelines, logging, observability, and incident management.
• Source of both production capability and production risk; SRE teams rely on AWS SLAs, telemetry, and control-plane behaviour.
• Frequent integration point for infrastructure-as-code, GitOps, and automated remediation.

Diagram description (text-only)

• Regions contain Availability Zones.
• Subnets and VPCs connect compute instances, containers, and serverless functions.
• Load balancers route traffic to autoscaling groups or services.
• Data stores include object storage, block volumes, relational and NoSQL databases.
• Observability pipeline gathers logs, metrics, and traces to monitoring and alerting systems.
• IAM controls access across services with roles and policies.
• External clients/users -> CDN edge -> Load balancer -> Service layer -> Datastore -> Monitoring/Audit systems.

AWS in one sentence

AWS is a public cloud platform that provides scalable infrastructure and managed services exposed via APIs to run applications, store data, and operate modern distributed systems.

AWS vs related terms (TABLE REQUIRED)

ID	Term	How it differs from AWS	Common confusion
T1	Azure	Microsoft cloud provider with different APIs and services	Mistaking service parity
T2	GCP	Google cloud provider focused on data and ML integrations	Assuming identical pricing
T3	On-premises	Customer-owned hardware in private location	Thinking identical ops model
T4	SaaS	Delivered app managed end-to-end	Treating SaaS as infrastructure
T5	IaaS	Base compute and networking primitives	Confusing IaaS with managed services
T6	PaaS	Managed runtime and platform services	Expecting full control like IaaS
T7	Kubernetes	Container orchestration platform often hosted on AWS	Equating EKS with raw AWS
T8	CDN	Edge caching network for content	Assuming CDN equals global compute
T9	Marketplace	Third-party software catalog on AWS	Believing marketplace items are fully supported

Row Details (only if any cell says “See details below”)

• None required

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Why does AWS matter?

Business impact (revenue, trust, risk)

• Revenue enablement: Rapid provisioning and global footprint let businesses reach customers faster.
• Trust and compliance: Managed compliance programs and regional controls help meet regulatory needs but require customer-side controls.
• Risk concentration: Vendor lock-in and misconfiguration risks can affect revenue and reputation if incidents occur.

Engineering impact (incident reduction, velocity)

• Velocity: Prebuilt managed services reduce time to market; teams iterate faster.
• Incident reduction: Managed services reduce operational burden but introduce blind spots around vendor outages or API changes.
• Technical debt shift: Less hardware debt, more configuration and integration debt.

SRE framing (SLIs/SLOs/error budgets/toil/on-call)

• SLIs can track service availability, latency, and error rates for AWS-hosted applications.
• Error budgets incorporate both customer-side and provider-side incidents.
• Toil is reduced for infrastructure provisioning but increases for cost optimization and permission governance.
• On-call must include runbooks for provider outages and control-plane failures.

3–5 realistic “what breaks in production” examples

• S3 misconfiguration leading to public data exposure or access failures.
• IAM policy scope too broad causing privilege escalation or accidental resource deletion.
• EBS snapshot process fails during backup window due to volume lock or API rate limits.
• Autoscaling misconfiguration causing scaling loops and resource exhaustion.
• Regional service outage affecting multi-region design when failover is incomplete.

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Where is AWS used? (TABLE REQUIRED)

ID	Layer/Area	How AWS appears	Typical telemetry	Common tools
L1	Edge / CDN	CloudFront and global edges	Cache hit ratio, latency	CDN configs, WAF
L2	Network	VPC, Transit Gateway, Direct Connect	VPC flow logs, LB metrics	VPC peering, route tables
L3	Compute	EC2, ECS, EKS, Lambda	CPU, memory, invocations	Autoscaling, AMIs
L4	Storage	S3, EBS, EFS	IOPS, throughput, errors	Lifecycle rules, backups
L5	Database	RDS, Aurora, DynamoDB	Query latency, retries	Backups, read replicas
L6	App Platform	Elastic Beanstalk, App Runner	Deploy success, errors	CI/CD hooks
L7	Observability	CloudWatch, X-Ray	Logs, traces, metrics	Exporters, agents
L8	Security	IAM, KMS, GuardDuty	Audit logs, anomalies	IAM policies, KMS keys
L9	CI/CD	CodeBuild, CodePipeline	Build status, duration	Git, pipelines
L10	Management	CloudFormation, CDK, Config	Drift, stack events	IaC tools, templates

Row Details (only if needed)

• None required

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When should you use AWS?

When it’s necessary

• Global reach with regional residency requirements.
• Need for specific managed services only AWS provides (when organizational dependency is acceptable).
• Rapid scale with managed operations and predictable compliance programs.

When it’s optional

• Standard web applications with modest scale where cloud vs managed hosting both work.
• Non-critical workloads that tolerate provider-specific architectures.

When NOT to use / overuse it

• When simple, low-cost dedicated hosting suffices and cloud complexity adds cost.
• When vendor lock-in risk outweighs benefits and portability is a strict requirement.
• When regulatory constraints forbid outsourcing certain data types.

Decision checklist

• If you need global regions + managed DB + auto-scaling -> Use AWS.
• If you need minimal infra, consistent multi-cloud portability -> Consider containerized platform on Kubernetes with strict IaC.
• If compliance requires physical separation or custom hardware -> On-premises or specialized provider.

Maturity ladder

• Beginner: Single account, simple EC2/EBS/S3, manual deploys.
• Goals: Basic IaC, backups, monitoring.
• Intermediate: Multiple accounts, IaC (CloudFormation/CDK/Terraform), automated CI/CD, RBAC.
• Goals: Multi-AZ, autoscaling, cost tagging, CI pipelines.
• Advanced: Multi-region DR, service mesh, automated remediation, policy-as-code, cost and security automation.
• Goals: Observability at scale, SLO-driven operations, automated failover.

Example decision for small teams

• Small startup with a web app and limited ops staff: Use managed database (RDS), serverless functions (Lambda) for event processing, S3 for storage to minimize ops.

Example decision for large enterprises

• Large enterprise requiring isolation: Use multiple AWS accounts via Organizations, centralized logging and security accounts, Infrastructure-as-Code, and strict guardrails.

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How does AWS work?

Components and workflow

1. Identity and access control (IAM) defines who can act on which resources.
2. Networking (VPC, subnets, route tables, security groups) controls connectivity.
3. Compute (EC2/ECS/EKS/Lambda) runs workloads across AZs.
4. Storage and databases store state (S3, EBS, RDS, DynamoDB).
5. Management plane (CloudFormation, Config, Organizations) governs resource lifecycle.
6. Observability (CloudWatch, X-Ray, CloudTrail) captures telemetry and audit trails.
7. Security services (KMS, GuardDuty) provide encryption and threat detection.

Data flow and lifecycle

• Ingress: Client request enters via CDN or load balancer.
• Processing: Request routed to compute service; compute may read/write storage or DB.
• Persistence: Writes committed to database/storage; backups or replication may occur.
• Observability: Logs, metrics, traces emitted to telemetry pipeline and stored.
• Egress: Responses returned to client via edge caches or direct network.

Edge cases and failure modes

• Control-plane rate limits block provisioning during scale-out.
• API inconsistency across regions causes feature gaps.
• Cold-start latency for serverless functions impacts SLIs.
• Billing spikes due to runaway resources or misconfigurations.

Short practical examples (pseudocode)

• Deploy a new service: define CloudFormation/TF module -> create role and permissions -> build container image -> push to ECR -> deploy to ECS/EKS -> configure ALB -> create health checks -> define autoscaling policies.

Typical architecture patterns for AWS

• Three-tier web app: CDN -> ALB -> Autoscaling EC2/ECS -> RDS -> S3 for assets. Use when migrating monoliths or lift-and-shift.
• Serverless event-driven: API Gateway -> Lambda -> DynamoDB/S3 -> SNS/SQS for async. Use when rapid dev and event processing needed.
• Container platform: EKS with nodegroups or Fargate for pods -> service mesh -> external secrets. Use when Kubernetes ecosystem is required.
• Data lake: S3 as landing zone -> Glue for ETL -> Athena for querying -> Lake Formation for governance. Use for large analytic workloads.
• Multi-account security baseline: Central security account -> shared logging account -> workload accounts with SCPs and IAM boundaries. Use for enterprise governance.

Failure modes & mitigation (TABLE REQUIRED)

ID	Failure mode	Symptom	Likely cause	Mitigation	Observability signal
F1	Regional outage	Multiple services unavailable	Region-level service failure	Multi-region failover design	Cross-region availability metrics
F2	IAM misconfig	Access denied or overprivilege	Incorrect IAM policy	Least privilege, policy review	CloudTrail auth errors
F3	Cost spike	Unexpected high bill	Orphaned resources or runaway tasks	Billing alerts, budget limits	Billing metric anomalies
F4	API rate limit	Provisioning errors	Excess API calls	Backoff, request batching	429 and throttling logs
F5	S3 public exposure	Data leak or audit alert	Bucket ACL or policy misconfig	Block public access, audits	S3 access logs, config
F6	EBS I/O saturation	Latency in VMs	High IO or wrong volume type	Right-size volumes, use provisioned IOPS	EBS IOPS and queue depth
F7	Lambda cold starts	High latency on cold requests	Unarchived runtime or VPC setup	Provisioned concurrency, warmers	Invocation duration distribution
F8	Autoscale thrashing	Constant scale up/down	Bad health check or metrics	Improve health checks, cooldowns	Scaling events and CPU trends

Row Details (only if needed)

• None required

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Key Concepts, Keywords & Terminology for AWS

• Account — Logical billing and resource boundary — Matters for isolation and billing — Pitfall: single account for prod and dev.
• Region — Geographical grouping of AZs — Matters for latency and compliance — Pitfall: assuming global replication.
• Availability Zone — Isolated data center within region — Matters for fault isolation — Pitfall: colocating all resources in one AZ.
• IAM — Identity and access management — Controls permissions — Pitfall: overly permissive policies.
• IAM Role — Temporary credentials attached to services — Enables least privilege — Pitfall: using long-lived keys.
• VPC — Virtual network — Segments network and controls routing — Pitfall: public subnet misconfiguration.
• Subnet — IP range within VPC — Controls placement — Pitfall: insufficient IP space.
• Security Group — Host-level firewall — Controls inbound/outbound rules — Pitfall: wide-open rules.
• NACL — Stateless subnet-level ACL — Additional layer — Pitfall: conflicting rules causing access issues.
• EC2 — Virtual machines — Core compute offering — Pitfall: not using autoscaling or right sizing.
• EBS — Block storage for EC2 — Persistent volumes — Pitfall: not snapshotting critical volumes.
• S3 — Object storage — Durable and scalable — Pitfall: misconfigured bucket policies.
• Glacier — Archive storage — Low-cost long-term retention — Pitfall: restore latency expectations.
• ELB/ALB/NLB — Load balancing options — Distribute traffic — Pitfall: incorrect target health checks.
• Route 53 — DNS and routing policies — Global traffic management — Pitfall: misrouted failover.
• RDS — Managed relational databases — Simplifies DB ops — Pitfall: relying on single AZ without replicas.
• Aurora — High-performance managed DB — Auto-scaling read replicas — Pitfall: cost vs benefit for small workloads.
• DynamoDB — Fully managed NoSQL store — High throughput and single-digit ms latency — Pitfall: hot partitioning.
• EKS — Managed Kubernetes — Runs K8s control plane — Pitfall: assuming AWS handles pod-level security.
• ECS — AWS container orchestration — Native integration — Pitfall: cluster scaling assumptions.
• Fargate — Serverless containers — No EC2 management — Pitfall: cold-starts and pricing for long-running tasks.
• Lambda — Serverless functions — Event-driven compute — Pitfall: cold starts and VPC networking latency.
• API Gateway — API front door — Manages APIs and auth — Pitfall: cost for high-volume small requests.
• CloudFront — CDN and edge caching — Reduces latency — Pitfall: invalidation cost and timing.
• WAF — Web application firewall — Protects at edge — Pitfall: false positives blocking legitimate traffic.
• CloudWatch — Monitoring and logs — Central telemetry — Pitfall: high log ingestion cost without filters.
• X-Ray — Distributed tracing — Traces request flows — Pitfall: sampling hides rare failures.
• CloudTrail — Audit logging for API calls — Forensics and compliance — Pitfall: not enabling multi-region trails.
• Config — Resource configuration tracking — Drift detection — Pitfall: missing rules for critical resources.
• Organizations — Account management at scale — Centralized policies — Pitfall: improper SCPs blocking needed actions.
• KMS — Key management service — Encryption keys — Pitfall: key policy misconfig locking access.
• Secrets Manager — Secrets lifecycle management — Rotates credentials — Pitfall: cost and overuse for static secrets.
• SQS — Durable message queue — Decouples systems — Pitfall: long polling misconfiguration.
• SNS — Pub/sub notifications — Fan-out patterns — Pitfall: lack of dead-letter handling.
• Glue — ETL and data catalog — Data processing — Pitfall: schema drift surprises.
• Athena — Serverless SQL over S3 — Ad-hoc analytics — Pitfall: unoptimized queries cost.
• Direct Connect — Dedicated network link — For high bandwidth/low latency — Pitfall: provisioning lead times.
• Transit Gateway — Centralized network hub — Simplifies VPC connectivity — Pitfall: complex routing tables.
• Backup — Managed backup services — Restore and retention — Pitfall: untested restores.
• GuardDuty — Threat detection — Alerts suspicious activity — Pitfall: alert fatigue without tuning.
• Inspector — Vulnerability assessments — Security scanning — Pitfall: ignoring prioritized findings.
• Cost Explorer — Cost analysis and allocation — Budgeting — Pitfall: not tagging resources.
• Savings Plans / Reserved Instances — Discounted compute pricing — Save cost with commitment — Pitfall: wrong commitment size.

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How to Measure AWS (Metrics, SLIs, SLOs) (TABLE REQUIRED)

ID	Metric/SLI	What it tells you	How to measure	Starting target	Gotchas
M1	Availability SLI	User-facing uptime	Successful responses / total requests	99.9% for customer APIs	Includes provider downtime
M2	Request latency P95	User latency experience	Measure response time distribution	P95 < 300ms for APis	Backend retries inflate latency
M3	Error rate	Fraction of failed requests	5xx and client-visible errors / requests	< 0.1% for mature services	Retry storms hide root cause
M4	Cold-start rate	Fraction of slow serverless starts	Count invocations with high init time	< 1% with provisioned concurrency	VPC functions slower
M5	Deployment failure rate	Failed deploys per release	Failed deploys / total deploys	< 1% deploys fail	Incomplete health checks mislabel
M6	Mean time to recover	Time to restore service	Time from incident start to recovery	< 30m for critical services	Detection delays lengthen MTTR
M7	Cost per transaction	Cost efficiency	Cloud cost / successful transactions	Varies / depends	Allocation and tagging required
M8	IAM policy drift	Unauthorized access risk	Number of overly permissive policies	0 critical policies	Audit frequency needed
M9	Backup success rate	Data protection health	Successful backups / scheduled backups	100% for critical data	Restores untested
M10	Control-plane error rate	Provisioning reliability	Failed API calls for infra ops	< 0.5%	Rate limits and throttles

Row Details (only if needed)

• None required

Best tools to measure AWS

Tool — CloudWatch

• What it measures for AWS: Metrics, logs, alarms, synthetic checks.
• Best-fit environment: Native AWS workloads.
• Setup outline:
• Instrument SDKs to emit custom metrics.
• Configure log groups and retention.
• Create dashboards and composite alarms.
• Set up metric filters and anomaly detection.
• Strengths:
• Native integration, low friction.
• Broad service coverage.
• Limitations:
• Cost scales with logs and custom metrics.
• Less flexible query language than external tools.

Tool — Prometheus

• What it measures for AWS: Time series metrics from apps and exporters.
• Best-fit environment: Kubernetes, containerized workloads.
• Setup outline:
• Deploy Prometheus operator or helm chart.
• Use node_exporter and aws_exporter for infra metrics.
• Configure Alertmanager and rules.
• Strengths:
• Powerful query language.
• Flexible alerting and federation.
• Limitations:
• Not ideal at extreme scale without long-term storage.
• Requires management for HA.

Tool — Datadog

• What it measures for AWS: Metrics, traces, logs, synthetic checks across services.
• Best-fit environment: Hybrid multi-cloud and SaaS-friendly.
• Setup outline:
• Connect AWS integration and enable services.
• Install agents on hosts and sidecars.
• Configure dashboards and monitors.
• Strengths:
• Unified observability across layers.
• Rich integrations and AI-assisted insights.
• Limitations:
• Costly at high cardinality.
• Dependency on external vendor.

Tool — OpenTelemetry + Tempo/OTel Collector

• What it measures for AWS: Traces and distributed context.
• Best-fit environment: Microservices and tracing-first stacks.
• Setup outline:
• Instrument apps with OpenTelemetry SDKs.
• Configure collectors and exporters.
• Store traces in compatible backend.
• Strengths:
• Vendor-neutral and flexible.
• High-fidelity distributed tracing.
• Limitations:
• Requires pipeline and storage choices.
• Sampling and storage costs to manage.

Tool — AWS X-Ray

• What it measures for AWS: Distributed tracing across supported AWS services.
• Best-fit environment: AWS-native microservices and Lambda.
• Setup outline:
• Enable X-Ray SDK or agent.
• Configure sampling and service map.
• Integrate with CloudWatch dashboards.
• Strengths:
• Native tracing for AWS services.
• Simple service maps.
• Limitations:
• Sampling may hide rare failures.
• Less flexible than open standards.

Recommended dashboards & alerts for AWS

Executive dashboard

• Panels:
• Overall availability vs SLO.
• Monthly cost trend and top services.
• Active incidents and error budget burn rate.
• Security alerts trend.
• Why: High-level view for leadership to track system health and cost.

On-call dashboard

• Panels:
• Current alerts grouped by severity.
• Service health and error rates.
• Recent deploys and rollback status.
• Top failing endpoints and traces.
• Why: Rapid triage for responders.

Debug dashboard

• Panels:
• Per-service latency percentiles (P50/P95/P99).
• Recent failed transactions with traces.
• Resource utilization (CPU, memory, IOPS).
• Dependency call graphs and downstream errors.
• Why: Deep investigation and RCA.

Alerting guidance

• Page vs ticket:
• Page for SLO-critical outages and when error budget burn exceeds thresholds.
• Ticket for degraded non-critical metrics or actionable ops work.
• Burn-rate guidance:
• Alert when burn rate reaches 4x for short windows or sustained 2x burn.
• Noise reduction tactics:
• Deduplicate alerts by grouping by service and error type.
• Use suppression windows for planned maintenance.
• Implement alert thresholds that consider request volume.

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Implementation Guide (Step-by-step)

1) Prerequisites – AWS account structure (Organizations, accounts for prod/non-prod/logging/security). – Identity model and MFA for admins. – Billing and cost center tagging policies. – Baseline networking design (VPC, subnets, NAT, internet gateway). – Terraform/CloudFormation/CDK repository and CI pipeline.

2) Instrumentation plan – Decide telemetry strategy: CloudWatch vs Prometheus vs hybrid. – Standardize common metrics, trace spans, and log formats. – Include distributed tracing libraries and middleware in app templates.

3) Data collection – Set up log forwarding agents (Fluentd/Fluent Bit) to chosen log store. – Configure metric exporters and custom metrics. – Enable CloudTrail and VPC Flow Logs for audit and forensic needs.

4) SLO design – Define user journeys and map to SLIs. – Choose SLO targets and error budgets per service. – Create monitoring rules tied to SLO burn rates.

5) Dashboards – Create executive, on-call, and debug dashboards per service. – Include SLO panels and recent deploy markers.

6) Alerts & routing – Set up on-call rotations and escalation policies. – Create alerting thresholds mapped to error budgets. – Integrate with pager and ticketing systems.

7) Runbooks & automation – Write runbooks for frequent incidents with step-by-step remediation. – Automate common remediations with Lambda or automation runbooks. – Implement automated rollbacks for failed deploys.

8) Validation (load/chaos/game days) – Run load tests to validate autoscaling and SLOs. – Execute chaos tests (AZ failure, instance termination) and verify failover. – Schedule game days and review postmortems.

9) Continuous improvement – Review incident metrics weekly and refine runbooks. – Optimize cost with rightsizing and reservation usage. – Periodically audit IAM, network, and telemetry coverage.

Checklists

Pre-production checklist

• IaC template validated and code-reviewed.
• CI pipeline passing build and integration tests.
• Service account, IAM roles, and least-privilege policies created.
• Observability instrumentation present and tested.
• Load tests executed with acceptable SLO results.

Production readiness checklist

• Multi-AZ or multi-region deployment as required.
• Backups and restore tested.
• On-call and escalation configured.
• Cost alerts and budget thresholds set.
• Security reviews and vulnerability scans completed.

Incident checklist specific to AWS

• Verify alert validity and check deployment markers.
• Check CloudWatch, CloudTrail, service health dashboard, and dependent services.
• Identify scope: account, AZ, region, or service-specific.
• If provider-side, escalate to AWS support and follow their guidance.
• Execute documented remediation and communicate status to stakeholders.

Examples

• Kubernetes example: For EKS, ensure nodegroups autoscaling, pod disruption budgets, horizontal pod autoscaler configured, Prometheus metrics scraped, and EBS CSI snapshots validated.
• Managed cloud service example: For RDS, enable automated backups, multi-AZ failover, enhanced monitoring, and set up read replicas for scaling.

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Use Cases of AWS

1) Global web storefront – Context: Retail company serving global customers. – Problem: Low-latency delivery and peak seasonal traffic. – Why AWS helps: CDN, global regions, autoscaling, managed DB replicas. – What to measure: End-user latency, checkout success rate, DB replica lag. – Typical tools: CloudFront, ALB, RDS/Aurora, Auto Scaling.

2) Event-driven order processing – Context: Microservices architecture processing orders. – Problem: Loose coupling and scalable processing. – Why AWS helps: Serverless queues and functions for decoupled scaling. – What to measure: Queue backlog, processing latency, error rate. – Typical tools: SQS, Lambda, SNS, DynamoDB.

3) Data lake analytics – Context: Analytics team requiring large-scale queries. – Problem: Store and query petabytes with cost control. – Why AWS helps: S3 for storage, Athena for serverless querying. – What to measure: Query cost per TB, job completion time, data freshness. – Typical tools: S3, Glue, Athena, Lake Formation.

4) Machine learning model training – Context: Training large models needing GPUs. – Problem: Large compute and data throughput needs. – Why AWS helps: On-demand GPU instances and managed training runtimes. – What to measure: Training time, cost per epoch, GPU utilization. – Typical tools: EC2 GPU instances, SageMaker, S3.

5) CI/CD pipeline – Context: Frequent deployments with automated testing. – Problem: Reproducible builds and deploys. – Why AWS helps: Managed build and pipeline integrations. – What to measure: Build time, deploy success rate, mean time to deploy. – Typical tools: CodeBuild, CodePipeline, ECR.

6) High-throughput API backend – Context: Real-time API with unpredictable load. – Problem: Autoscaling and cost control. – Why AWS helps: Autoscaling groups, serverless scaling, managed caches. – What to measure: P95 latency, error rate, cache hit ratio. – Typical tools: ALB, EC2/ECS/Fargate, ElastiCache.

7) Backup and archival – Context: Long-term retention and compliance. – Problem: Cost-effective archival with retrieval capability. – Why AWS helps: Glacier and object lifecycle policies. – What to measure: Backup success, restore time, retention compliance. – Typical tools: S3, Glacier, Backup service.

8) Multi-tenant SaaS platform – Context: SaaS provider hosting many customers. – Problem: Isolation, cost efficiency, automation. – Why AWS helps: Account segmentation, IAM, tagging, managed services. – What to measure: Tenant isolation incidents, cost per tenant, provisioning time. – Typical tools: Organizations, IAM, CloudFormation, Lambda.

9) Edge computing with low latency – Context: IoT devices requiring near-edge compute. – Problem: Latency and intermittent connectivity. – Why AWS helps: Edge runtimes and Greengrass or Lambda@Edge. – What to measure: Edge execution success, synchronization lag. – Typical tools: AWS IoT, Greengrass, CloudFront.

10) Disaster recovery – Context: Business continuity plan for critical workloads. – Problem: RTO/RPO compliance across regions. – Why AWS helps: Cross-region replication and failover services. – What to measure: RTO, RPO, failover success rate. – Typical tools: S3 replication, RDS cross-region replicas, Route 53 failover.

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Scenario Examples (Realistic, End-to-End)

Scenario #1 — Kubernetes blue/green deploy on EKS

Context: E-commerce team wants safer deploys with no downtime. Goal: Deploy new service versions with immediate rollback capability. Why AWS matters here: EKS provides managed control plane and integrates with ALB for traffic shifting. Architecture / workflow: Git -> CI builds image -> push to ECR -> update Kubernetes manifests -> create new deployment and service -> ALB ingress route switch. Step-by-step implementation:

1. Create new namespace and deployment with new image tag.
2. Use service selector or ingress weight annotation to shift 10% traffic.
3. Run smoke tests against new pods.
4. Gradually increase traffic if tests pass, otherwise roll back. What to measure: Error rate, latency, deployment failure rate. Tools to use and why: EKS for orchestration, Argo CD for GitOps, ALB for traffic weighting, Prometheus for metrics. Common pitfalls: Not using readiness probes causing traffic to hit unready pods. Validation: Confirm no user-facing errors during shift and rollback speed under 5 minutes. Outcome: Reduced deployment risk and faster recovery from bad releases.

Scenario #2 — Serverless image processing pipeline

Context: Media company processes user uploads into multiple sizes. Goal: Scalable, cost-efficient processing with no server management. Why AWS matters here: Lambda and S3 tightly integrate and scale automatically. Architecture / workflow: Upload to S3 -> S3 event triggers Lambda -> Lambda processes and writes variants back -> SNS notification on completion. Step-by-step implementation:

1. Configure S3 event to invoke Lambda.
2. Implement concurrency limits and retries for Lambda.
3. Store processed images in different S3 prefixes.
4. Send completion messages to downstream services. What to measure: Processing latency, failure rate, cold-starts. Tools to use and why: S3 for storage, Lambda for compute, SNS/SQS for retries, CloudWatch for metrics. Common pitfalls: Lambda VPC attachment causing increased cold-start latency. Validation: Load test with bursts of concurrent uploads and verify processing time and success rate. Outcome: Lower ops overhead and elastic processing.

Scenario #3 — Incident response for cross-AZ DB failover

Context: Production RDS primary instance becomes unreachable in AZ A. Goal: Failover to standby in AZ B with minimal downtime. Why AWS matters here: RDS supports automated multi-AZ failover but requires readiness. Architecture / workflow: RDS primary -> multi-AZ standby -> Route 53 and connection strings point to cluster endpoint. Step-by-step implementation:

1. Detect failover via RDS events and CloudWatch alarm.
2. Promote standby if automatic failover not performed.
3. Re-route read replicas if needed and verify application reconnection.
4. Investigate root cause and perform postmortem. What to measure: RTO, RPO, failover success rate, application error rate. Tools to use and why: RDS monitoring, CloudWatch alarms, CloudTrail for API actions. Common pitfalls: Hard-coded instance endpoints in app configs preventing automatic failover. Validation: Regular DR drill failover and restore tests. Outcome: Faster recovery with validated failover processes.

Scenario #4 — Cost-performance tuning for ML training

Context: Data science team training models nightly on GPU instances. Goal: Reduce training cost while keeping throughput acceptable. Why AWS matters here: AWS offers many instance types and spot options to optimize cost. Architecture / workflow: Training job scheduler -> EC2 GPU fleet (spot+on-demand) -> S3 data store -> EBS for local caching. Step-by-step implementation:

1. Benchmark training across instance types for throughput.
2. Implement spot instances with interruption handling.
3. Use S3 dataset caching and instance-local scratch.
4. Schedule training during lower spot price windows. What to measure: Cost per training run, training runtime, interruption rate. Tools to use and why: EC2, S3, SageMaker for managed training, Spot Fleet. Common pitfalls: Not checkpointing leading to lost work on spot interruptions. Validation: Run controlled experiments to measure cost savings and interruption overhead. Outcome: Lowered cost per model with acceptable runtime trade-offs.

Scenario #5 — Postmortem of a public S3 bucket exposure

Context: Public data detected in a customer bucket. Goal: Remediate exposure and fix root cause to prevent recurrence. Why AWS matters here: S3 ACL and bucket policy model and management plane are central to exposure risk. Architecture / workflow: S3 stores assets -> IAM roles and bucket policies control access -> CloudTrail records policy changes. Step-by-step implementation:

1. Immediately lock bucket via policy to deny public access.
2. Identify impacted objects and rotate keys if secrets leaked.
3. Review CloudTrail and Config for change timeline.
4. Implement guardrails: block public access, policy linting in CI, automated policy checks. What to measure: Time to remediation, number of public objects, audit trail completeness. Tools to use and why: S3 Block Public Access, AWS Config rules, CloudTrail, automated IaC checks. Common pitfalls: Assuming block public access applies across all accounts or buckets. Validation: Scheduled checks to ensure no new public buckets exist and run simulated misconfiguration tests. Outcome: Restored confidentiality and improved preventive controls.

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Common Mistakes, Anti-patterns, and Troubleshooting

1) Symptom: Unexpected access denied errors -> Root cause: Overly restrictive IAM policy -> Fix: Use policy simulator and incremental least-privilege grants. 2) Symptom: High S3 costs -> Root cause: No lifecycle rules -> Fix: Add lifecycle rule to transition cold objects to archival storage. 3) Symptom: Slow API responses -> Root cause: Lambda cold starts in VPC -> Fix: Use provisioned concurrency or move out of VPC. 4) Symptom: Autoscaling flapping -> Root cause: Short cooldown and noisy metrics -> Fix: Increase cooldown and use stabilized metrics. 5) Symptom: Lost logs after rotation -> Root cause: Log retention misconfiguration -> Fix: Set centralized logging retention and export pipelines. 6) Symptom: Frequent 429 throttles -> Root cause: Exceeding API rate limits -> Fix: Implement exponential backoff and batch API calls. 7) Symptom: Massive bill spike -> Root cause: Orphaned or runaway resources -> Fix: Tagging, budget alarms, automated shutdown for dev accounts. 8) Symptom: DB replica lag -> Root cause: Write amplification or insufficient replicas -> Fix: Scale reads, optimize queries, use read-only replicas. 9) Symptom: Service not failing over -> Root cause: Hard-coded AZ endpoints -> Fix: Use DNS-based endpoints and connection strings. 10) Symptom: Flaky deploys -> Root cause: Not testing migrations in staging -> Fix: Run migration tests with production-sized data. 11) Symptom: Missing traces -> Root cause: Sampling settings too aggressive -> Fix: Adjust sampling rate or capture critical transactions. 12) Symptom: Noisy alerts -> Root cause: Alerts on raw metrics without SLO context -> Fix: Move to SLO-driven alerts and use aggregation. 13) Symptom: Secrets leaked in logs -> Root cause: Logging unredacted environment variables -> Fix: Implement log sanitization and secrets manager usage. 14) Symptom: Slow backups -> Root cause: Snapshots of busy volumes -> Fix: Use application-consistent backups and stagger windows. 15) Symptom: Unauthorized resource creation -> Root cause: Weak org SCPs -> Fix: Harden SCPs and set Guardrails in Organizations. 16) Symptom: Inefficient queries in Athena -> Root cause: Unpartitioned or uncompressed data -> Fix: Partition and compress datasets. 17) Symptom: EKS pods stuck pending -> Root cause: Insufficient node capacity or taints -> Fix: Adjust node autoscaler, pod tolerations, and quotas. 18) Symptom: Metric gaps -> Root cause: Metric emission failures or agent restarts -> Fix: Ensure agent restart policies and metric buffering. 19) Symptom: Inconsistent config across accounts -> Root cause: Manual changes -> Fix: Enforce Config rules and automated IaC deploys. 20) Symptom: Expensive cross-region data transfer -> Root cause: Chatty service design -> Fix: Co-locate services or use caching and replication. 21) Observability pitfall: Missing request context in logs -> Root cause: Not propagating trace IDs -> Fix: Inject trace IDs into logs at request entry. 22) Observability pitfall: Too many low-value custom metrics -> Root cause: No metric lifecycle policy -> Fix: Catalog metrics and retire low-value ones. 23) Observability pitfall: Traces sampled inconsistently -> Root cause: Sampler mismatch across services -> Fix: Centralize sampling policy and use head-based sampling. 24) Symptom: Unreadable CloudTrail logs -> Root cause: No log encryption or key access issues -> Fix: Use KMS policies and test decrypt workflows. 25) Symptom: Frequent IAM role expiration issues -> Root cause: Long-lived credentials in CI -> Fix: Use role assumption via STS and short-lived tokens.

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Best Practices & Operating Model

Ownership and on-call

• Define ownership per service with a single Pager for incidents and a separate escalation chain.
• Rotate on-call and ensure knowledge transfer between shifts.

Runbooks vs playbooks

• Runbooks: Step-by-step recovery for common incidents.
• Playbooks: Higher-level decision guides for complex incidents.

Safe deployments

• Canary and blue/green deployments for large traffic services.
• Automated rollback triggers on health-check or SLO breaches.

Toil reduction and automation

• Automate repetitive tasks: backups, snapshotting, cost reports, IAM scans.
• Implement remediation runbooks triggered by alerts.

Security basics

• Enforce least privilege, MFA on privileged accounts, key rotation, and automated vulnerability scanning.

Weekly/monthly routines

• Weekly: Review critical alerts, on-call handover, deploy frequency, cost anomalies.
• Monthly: Billing review, IAM policy audit, security scans, SLO health review.

What to review in postmortems related to AWS

• Root cause including config and provider factors.
• Time-to-detection and time-to-recovery.
• Mitigations applied and follow-up actions.
• Impact on SLOs and error budget consumption.

What to automate first

• Tagging and cost allocation policies enforcement.
• Alert deduplication and suppression for known maintenance windows.
• Automated backups and restore verification.
• IAM policy linting and policy enforcement as code.

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Tooling & Integration Map for AWS (TABLE REQUIRED)

ID	Category	What it does	Key integrations	Notes
I1	IaC	Provision resources as code	CloudFormation, Terraform, CDK	Use CI for deploys
I2	CI/CD	Build and deploy pipelines	CodeBuild, GitHub Actions	Integrate tests and gating
I3	Observability	Metrics, logs, traces	CloudWatch, Prometheus, OpenTelemetry	Centralized dashboards
I4	Security	Threat detection and scans	GuardDuty, Inspector, IAM	Automate findings triage
I5	Cost mgmt	Analyze and forecast spend	Cost Explorer, Budgets	Tagging critical
I6	Secrets	Manage credentials	Secrets Manager, Parameter Store	Rotate secrets regularly
I7	Networking	Connect and route traffic	VPC, Transit Gateway, Route 53	Plan IP and routing
I8	Storage	Durable data storage	S3, EFS, EBS	Lifecycle policies
I9	Databases	Managed DB services	RDS, DynamoDB, Aurora	Backup and scaling
I10	Serverless	Event-driven compute	Lambda, API Gateway	Use provisioned concurrency
I11	Containers	Container orchestration	EKS, ECS, Fargate	Implement pod security
I12	Data infra	ETL and analytics	Glue, Athena, Redshift	Optimize storage layout
I13	Edge	CDN and edge compute	CloudFront, Lambda@Edge	Use for latency-sensitive content
I14	Backup	Managed backups and restores	Backup service, snapshots	Test restores often

Row Details (only if needed)

• None required

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Frequently Asked Questions (FAQs)

How do I reduce AWS costs quickly?

Start with tagging and cost allocation, identify top spenders, rightsizing instances, and using savings plans or reserved instances for steady-state usage.

How do I migrate a database to AWS with minimal downtime?

Use native replication tools or logical replication to a managed DB, promote target at cutover, and validate data consistency.

How do I secure AWS credentials used in CI/CD?

Use IAM roles with STS assumed roles and short-lived credentials, and store secrets in Secrets Manager with rotation.

What’s the difference between EC2 and Lambda?

EC2 provides VM-level control and long-running processes; Lambda is serverless, event-driven, and abstracts server management.

What’s the difference between EKS and ECS?

EKS runs upstream Kubernetes control plane; ECS is AWS-native container orchestration. EKS offers portability; ECS has tighter AWS integration.

What’s the difference between S3 Standard and Glacier?

S3 Standard is for frequent access; Glacier is archival for infrequent access with higher retrieval latency.

How do I measure availability for an AWS-hosted service?

Define an SLI (successful requests/total requests) and monitor using synthetic checks and real user monitoring.

How do I handle AWS provider outages?

Design for multi-AZ and multi-region redundancy where required; have runbooks for provider-side incidents and use cross-region replication.

How do I implement disaster recovery with RPO/RTO constraints?

Choose replication strategy (cross-region replicas, backups), test restores, and automate failover steps in runbooks.

How do I monitor serverless functions effectively?

Track invocation counts, error rates, cold-start latency, and downstream dependency latencies with traces and logs.

How do I audit changes in my AWS environment?

Enable CloudTrail multi-region, configure Config rules for drift detection, and centralize logs for alerting.

How do I migrate on-premises workloads to AWS?

Assess dependencies, replatform where appropriate, pick migration waves, and use network links like Direct Connect.

How do I choose between managed DB and self-managed on EC2?

Use managed DB for reduced ops and built-in HA; choose self-managed for custom extensions or unsupported engines.

How do I avoid vendor lock-in with AWS services?

Abstract critical logic, use open standards, and design for portability where business needs demand it.

How do I set SLOs for a new service?

Identify user journeys, pick SLIs that map to user experience, choose realistic targets based on historical data or benchmarks.

How do I secure S3 buckets from accidental public access?

Enable Block Public Access, enforce bucket policies, and scan repositories for accidental ACLs.

How do I reduce noise in CloudWatch alarms?

Aggregate events, use composite alarms, and align alarms to SLO thresholds rather than raw metric fluctuations.

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Conclusion

AWS is a broad, powerful cloud platform that, when applied with guardrails, observability, and SRE discipline, enables fast delivery and scalable operations. Success requires planning for security, cost, and reliability, plus continuous validation and automation.

Next 7 days plan

• Day 1: Inventory critical workloads, enable CloudTrail and basic monitoring.
• Day 2: Define account structure and implement IAM baseline with MFA.
• Day 3: Instrument one service with metrics, traces, and logs.
• Day 4: Create SLOs for a high-priority user journey and dashboard.
• Day 5: Run a smoke load test and verify autoscaling and backups.

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Appendix — AWS Keyword Cluster (SEO)

• Primary keywords
• AWS
• Amazon Web Services
• AWS cloud
• AWS services
• AWS architecture
• AWS best practices
• AWS security
• AWS cost optimization
• AWS monitoring

• AWS SRE

• Related terminology

• EC2
• S3
• RDS
• DynamoDB
• Lambda
• EKS
• ECS
• Fargate
• CloudFront
• CloudWatch
• CloudTrail
• IAM
• VPC
• Availability Zone
• Region
• Route 53
• KMS
• Secrets Manager
• CloudFormation
• Terraform on AWS
• CDK
• Autoscaling
• ELB ALB NLB
• SQS
• SNS
• Athena
• Glue
• Lake Formation
• Redshift
• SageMaker
• GuardDuty
• Inspector
• WAF
• Backup and restore AWS
• Cost Explorer
• Savings Plans
• Reserved Instances
• OpenTelemetry AWS
• X-Ray tracing
• API Gateway
• Lambda@Edge
• Direct Connect
• Transit Gateway
• EBS snapshots
• Glacier archival
• Elasticache Redis
• CloudWatch Logs
• Prometheus on EKS
• Observability AWS
• SLOs for cloud services
• Disaster recovery AWS
• Multi-region architecture
• Serverless architecture AWS
• Kubernetes EKS best practices
• AWS security best practices
• AWS governance and compliance
• AWS organizations guide
• Tagging strategies AWS
• Billing alarms AWS
• Infrastructure as code AWS
• GitOps with EKS
• Canary deployments on AWS
• Blue green deploy AWS
• AWS incident response
• Cost per transaction AWS
• Spot instances AWS
• GPU instances AWS
• ML training AWS
• Data lake on S3
• Athena query optimization
• CloudFront invalidation
• VPC flow logs
• CloudFormation drift detection
• IAM role assumption
• STS short-lived tokens
• S3 lifecycle policies
• AWS compliance programs
• Encryption KMS AWS
• RTO RPO AWS
• Backup testing procedures
• Managed database vs self-managed
• AWS Marketplace software
• Third-party integrations AWS
• Lambda cold start mitigation
• Autoscaling policies AWS
• Node autoscaler EKS
• Pod disruption budgets
• EBS performance tuning
• DynamoDB best practices
• Hot partition mitigation
• AWS logging pipeline
• Centralized logging AWS
• CloudWatch dashboards design
• Alert deduplication AWS
• Error budget burn rate
• Observability pitfalls AWS
• Security guardrails AWS
• Policy as code AWS
• Service control policies AWS
• AWS cost allocation tags
• AWS billing alerts
• Multi-account security AWS
• AWS support escalation process
• Provider outage handling AWS
• Cross-region replication S3
• Cross-region read replicas RDS
• AWS Greengrass edge computing
• Lambda VPC cold start
• Elastic IP management
• Transit Gateway architecture
• VPC peering vs Transit Gateway
• EKS control plane scaling
• Fargate use cases
• API throttling AWS
• 429 handling in AWS
• AWS service quotas management
• Quota increase requests AWS
• Backup snapshot policies AWS
• Continuous improvement AWS operations