Building an AWS Media Platform Around Bursty Workloads

Ampity designed and implemented an EKS media platform with separate data engines, automated releases and workflows, and shared monitoring across the operating layers.

[Problem] Media demand and background work compete for capacity

A media platform had to support traffic bursts while processing content and data. Sizing everything for the highest demand would leave capacity idle between spikes. Sizing only for ordinary demand would expose the platform when traffic increased. The engineering problem included application capacity, several kinds of data, release automation and operating visibility.

Ampity designed and implemented an AWS platform around those requirements. Containerized workloads ran on Amazon EKS across multiple Availability Zones. The engagement also included the data layer, streaming and media-processing services, GitOps releases, workflow orchestration, identity controls and observability. The customer remains unnamed in this public account.

This case covers that platform delivery. It does not establish that Kubernetes alone reduced cost or that moving a database to Aurora automatically improved every query. The source describes several decisions and their implementation order, so they are treated as a connected engineering effort rather than one service substitution.

[Decision] Give different data workloads different engines

The delivered platform included RDS and Aurora for relational data, ElastiCache Redis for in-memory state, MSK for streams, and S3 for object storage. MongoDB was separately hosted and connected through VPC peering. It should not be drawn inside the same AWS network boundary without qualification.

Kinesis, Firehose, Glue, Rekognition and Lambda supported the stream and media-processing side of the platform. The customer summary identifies the service inventory but does not document every dataset, producer, consumer or event contract. The revised figure therefore groups these responsibilities instead of inventing a detailed chain of data transfers.

That distinction is useful to another engineering team: a list of services can establish what was used without establishing exactly how every component communicated. A detailed implementation diagram requires those additional relationships and their failure behavior.

[Design] Keep runtime, change and visibility distinct

This is a responsibility map reconstructed from the delivered platform summary, not a subnet or packet-flow diagram. No connector between two groups should be inferred as a documented integration. The network topology and individual service relationships remain in the project's technical records.

[Delivery] Establish dependencies before relying on them

The source describes assessment and architecture design first. The core data layer was provisioned before application workloads depended on it. Container images, Argo and Airflow were then set up for release and workflow responsibilities, followed by the EKS deployment across Availability Zones.

Streaming and media-processing services followed, alongside the identity and security configuration. CloudWatch and OpenTelemetry were integrated across the platform, including EKS, Airflow, Argo and MongoDB. Monitoring was part of the delivery rather than a later addition to an otherwise completed system.

This order made the layers available for staged checks. It does not establish that every possible outage was tested or that all deployments were free of customer impact. The summary supplies the phase order and broad delivery responsibilities, not the complete test log or change history.

[Resilience] Several zones do not prove every recovery path

The application design distributed workloads across Availability Zones and used load-balancer health checks. That reduced dependence on a single zone, but an application recovery claim also depends on data availability, capacity in surviving zones, configuration and dependencies outside the cluster.

AWS explains the division of resilience responsibilities for EKS. The managed service and the customer's workloads have different responsibilities. This reference describes the service boundary; it does not independently verify a recovery test for this engagement.

This account describes the delivered architecture and operating work. A measured savings or recovery comparison would additionally require the workload, baseline, observation period and test conditions. Those are not supplied by the customer summary.

[Result] A platform with documented operating responsibilities

Ampity delivered the EKS runtime with its data, release, processing, security and observability layers. The resulting account explains where application execution, data handling, change and investigation belonged. It does not claim that managed services eliminated database administration, capacity planning or production support.

For another media platform, start by recording which workload creates each cost or saturation signal. Separate request-serving work from background processing, identify the authoritative store for each kind of data, and define the evidence required before changing capacity or a data engine.

Before adopting a similar design, check zone-loss behavior, external dependency availability, release recovery and the ownership of each alert. Use the customer's own workload and recovery objectives for those checks. The service inventory in this case is a description of delivered work, not a requirement to use every listed component. Explore Ampity's cloud engineering services.