Design, build, and operate workloads on AWS. Learn compute, networking, storage, IAM, databases, containers, serverless, observability, security, and cost control. Follow reference architectures and labs to ship resilient, scalable cloud solutions.
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Learn how Aurora PostgreSQL can query Apache Iceberg data in Amazon S3, allowing applications to combine operational and historical lake data without a traditional ETL pipeline.
Learn how to prepare, validate, transfer, and install frozen package sets for secure, reproducible updates on air-gapped AWS servers.
Learn how to audit AI-driven DevOps changes on AWS using CloudTrail, dedicated IAM roles, workflow logs, approvals, and controlled automation.
Introduction Vector search is useful when an application needs to find information by meaning rather than exact keywords. It is commonly used in semantic search, retrieval-augmented generation (RAG), recommendation systems, document search, and AI agents. But real applications rarely search every ve
Modern cloud applications rarely consist of a single service. A typical production system may contain APIs, background workers, databases, notification services, payment systems, analytics pipelines, and AI workloads. When these services communicate directly with one another, the architecture can qu
Large language models can generate powerful responses, but production inference introduces a different challenge: latency. An application may need to process thousands of requests while maintaining predictable response times. As traffic grows, teams often have multiple models, inference endpoints, w
Learn how DynamoDB Multi-Region Strong Consistency handles Regional failures, quorum, failover, write conflicts, recovery, RPO, and production design.
AI applications often depend on inference APIs to generate predictions, embeddings, summaries, classifications, and large language model responses. As traffic increases, inference latency can become one of the biggest challenges in production. A model may be fast when tested with a few requests, but
Learn how to monitor AWS DataSync using CloudWatch metrics, logs, task reports, and execution details without building unnecessary custom metric pipelines.
Learn how Amazon Transcribe works with AWS KMS customer managed keys, including encryption, key policies, IAM, permissions, monitoring, troubleshooting, and lifecycle.
Learn how to test AWS disaster recovery for Graviton workloads, including Arm64 compatibility, recovery drills, RTO, RPO, data validation, failover, and failback.
Learn how Amazon EventBridge retries, dead-letter queues, archives, ordering, and replay help recover failed events and build reliable event-driven systems.
Benchmark AWS Lambda Durable Workflows for .NET to measure failure recovery, execution latency, retry behavior, workflow reliability, resource usage, and serverless performance.
Build cost-aware retry policies for AWS Lambda .NET workloads to control retries, throttling, cold starts, duplicate processing, execution time, and serverless infrastructure costs.
Learn how to build least-privilege AWS IAM policies for AI coding agents that restrict dangerous actions, protect cloud resources, and enforce secure agent permissions.
Explore how AWS Lambda MicroVM isolation can securely execute AI-generated code while reducing risks from untrusted workloads, autonomous agents, and dynamic code execution.
Benchmark AWS Lambda Durable Execution for multi-step .NET workflows by measuring checkpoint overhead, recovery latency, execution performance, scalability, and operational cost.
Build resilient long-running .NET workflows with AWS Lambda Durable Execution using checkpoints, callbacks, waits, retries, and recovery for reliable distributed processing.
Benchmark AgentCore Runtime isolation for multi-tenant AI. Evaluate latency, resource use, and security for robust AI systems.
Build resilient .NET workflows on AWS Lambda with Durable Execution. Manage state, retries, and long waits directly in C# code.
Map your custom domain to AWS EC2. Learn to reserve Elastic IPs, configure security groups, update DNS, and secure with SSL/TLS.
Eliminate AWS Lambda cold starts for enterprise LangGraph RAG APIs. Optimize code, packaging, infrastructure, and architecture for sub-second latency.
Compare ARM vs x86 servers for cloud apps: explore performance, cost, power efficiency, and compatibility to optimize your infrastructure.
Ensure reliable event delivery in distributed systems with the Outbox Pattern. Achieve data consistency without complex distributed transactions.
Apache Iceberg: Revolutionize your data lake with ACID, schema evolution, time travel, and enhanced performance for massive analytics workloads.
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