Category Archives: Cloud Computing
Amazon S3 Multi-Region Access Points (MRAPs) provide a single global S3 endpoint in front of buckets located in multiple AWS Regions. Requests to that endpoint use the AWS global network and are routed toward an active bucket based on proximity and current routing state. For multi-Region applications, this replaces client-side region selection with one access-point […]
Amazon S3 Object Lock protects specific object versions from overwrite or deletion using a write-once-read-many (WORM) model. Current S3 supports fixed or variable retention periods, governance and compliance retention modes, legal holds, bucket-level default retention, and Batch Operations for applying retention at scale. Object Lock operates only with S3 Versioning, so the immutable unit is […]
Amazon ECS capacity providers connect task placement with the compute supply that runs those tasks. Instead of choosing a launch type directly, a service or RunTask request can use a capacity provider strategy that distributes tasks across Fargate, Fargate Spot, or one or more Auto Scaling group capacity providers. For EC2-backed clusters, managed scaling can […]
AWS KMS multi-Region keys are related KMS keys in different AWS Regions that share the same key ID, key material, key spec, key usage, key-material origin, and rotation state. Because related keys are cryptographically interoperable, data encrypted under one related key can be decrypted by another related key in a different Region without re-encrypting or […]
CloudFront origin failover lets a distribution use a secondary origin when the primary origin cannot serve an eligible request. The feature is configured through an origin group with a primary and secondary origin plus one or more failover criteria such as connection failure or selected HTTP status codes. Inside AWS Architecture and Operations, origin failover […]
CloudWatch Application Signals gives teams an application-centric view of service health by discovering services and dependencies, collecting standard metrics and traces, and letting operators define service level objectives. Instead of starting with hundreds of infrastructure metrics, the team can start with a service or operation and ask whether it is available, fast enough, and meeting […]
CloudWatch Metric Math lets teams combine, transform, and compare CloudWatch metrics into new time series. A simple expression can calculate error rate from errors and invocations, convert bytes to megabytes, compare used capacity with a limit, or compute a service-level indicator that is more meaningful than any raw metric alone. Inside AWS Architecture and Operations, […]
AWS cost allocation tags turn technical resource metadata into billing dimensions that can be used in Cost Explorer, Cost and Usage Reports, Budgets, Cost Categories, and other cost-management workflows. The hard part at scale is not creating one tag. It is maintaining a small, stable tagging vocabulary across accounts and services so costs remain attributable […]
AWS Direct Connect resiliency depends on more than having two circuits. The location, device, provider path, BGP design, virtual-interface configuration, and failover testing all determine whether a second connection is actually independent enough to carry traffic when the first path fails. Inside AWS Architecture and Operations, Direct Connect is the hybrid-network path that connects on-premises […]
EC2 Auto Scaling warm pools reduce scale-out latency for applications whose instances take a long time to initialize. Instead of launching every instance from zero when demand rises, Auto Scaling can maintain a pool of pre-initialized instances in stopped, hibernated, or running states and move them into service as needed. Inside AWS Architecture and Operations, […]
AI on Google Cloud is no longer one product path. Production systems can combine Gemini models on Vertex AI, Agent2Agent interoperability, Agent Engine or Cloud Run for agent execution, BigQuery and AlloyDB for retrieval, Cloud SQL for application-owned vectors, Vertex AI Feature Store for low-latency features, and managed safety, grounding, evaluation, monitoring, and cost controls […]
The Agent2Agent protocol is an open standard for communication between independent AI agents. Instead of one orchestrator knowing the private implementation details of every remote agent, the remote agent publishes an agent card that describes its interface and capabilities. Clients can then send messages, stream responses, and work with the remote system through a protocol […]
Vertex AI batch prediction is for inference workloads where results can be produced asynchronously over many inputs instead of one request at a time. The job reads data from a durable source such as Cloud Storage or BigQuery, runs predictions, and writes results to an output location that can be processed later. Within AI on […]
Feature-store design is about keeping model inputs consistent across training and serving. In Google Cloud’s current architecture, BigQuery feature tables act as the data source, while Vertex AI Feature Store can synchronize selected feature views into an online store for low-latency serving. Offline workflows continue to use BigQuery directly. Within AI on Google Cloud, Feature […]
Gemini context caching reduces the cost and latency of repeatedly processing large input context. If many requests share the same long document set, media asset, code base, or system context, the application can benefit when the model reuses previously processed tokens instead of treating the entire context as new on every call. Within AI on […]
