Project Overview
| Client Industry | Media / Video Streaming |
| Business Type | Regional video-on-demand streaming service, ~85,000 subscribers |
| Project Duration | 20 weeks |
| AI Service Provided | Cloud Infrastructure & Migration |
| Technologies Used | AWS (EC2, S3, CloudFront, RDS, ElastiCache, MediaConvert), Terraform, Docker, GitHub Actions, Datadog |
The Client Challenge
The client ran its entire streaming platform video transcoding, content delivery, subscriber database, and billing on aging on-premises servers housed in a single data center.
The operational pain points:
- The data center had no redundancy; a single hardware failure the previous year caused an 11-hour platform-wide outage during a high-traffic weekend.
- Video transcoding for new content releases took 6–8 hours per title on the existing hardware, delaying content availability.
- Scaling for traffic spikes (new season releases, marketing campaigns) wasn’t possible the infrastructure was fixed capacity, leading to buffering and failed streams during peak demand.
- The single data center location meant subscribers outside the region experienced high latency and poor streaming quality.
- The client’s small 3-person infrastructure team spent most of their time on reactive server maintenance rather than platform improvements.
- Leadership needed to migrate to the cloud but couldn’t accept downtime the platform needed to stay live and serving subscribers throughout the transition.
Our Solution
Air Brite Labs planned and executed a phased, zero-downtime migration of the client’s entire platform to AWS, rebuilding the infrastructure for redundancy, global content delivery, and elastic scale.
Rather than a “lift and shift,” the migration modernized the architecture where it mattered most: transcoding moved to a managed, scalable service, content delivery moved to a global CDN, and the subscriber/billing database moved to a managed, multi-AZ database service.
Key capabilities:
- Global content delivery — video content now serves through CloudFront’s edge network, dramatically improving load times for subscribers outside the original service region.
- Elastic transcoding — new content transcodes through AWS MediaConvert, scaling automatically with content volume instead of being limited by fixed on-prem hardware.
- Multi-AZ database redundancy — subscriber and billing data now runs on Multi-AZ RDS, eliminating the single-point-of-failure risk that caused the prior outage.
- Auto-scaling application tier — the application layer scales automatically during traffic spikes instead of hitting a hard capacity ceiling.
- Zero-downtime cutover — the entire migration was executed using a parallel-run and gradual traffic-shifting strategy, with no subscriber-facing downtime.
Technical Approach
Migration Strategy: A phased, service-by-service migration using a strangler pattern — new AWS infrastructure was stood up alongside the existing on-prem system, with traffic gradually shifted service by service rather than a single cutover event.
Compute & Application Tier: Application services containerized with Docker and deployed on auto-scaling EC2 instance groups behind an Application Load Balancer, replacing fixed-capacity on-prem servers.
Media Pipeline: Raw video uploads route to S3, triggering AWS Media Convert jobs for transcoding into multiple bitrates/formats, with output served through CloudFront replacing the manual, hardware-bound transcoding process entirely.
Database Migration: Subscriber and billing data migrated to Multi-AZ RDS using AWS Database Migration Service for continuous replication during the transition, allowing the cutover to happen with minimal, sub-second data lag rather than a lengthy maintenance window.
Caching: ElastiCache (Redis) introduced for session data and frequently accessed content metadata, reducing database load and improving application response times.
Infrastructure as Code: All new AWS infrastructure defined in Terraform from day one, replacing the client’s prior undocumented, manually configured server setup with version-controlled, repeatable infrastructure.
Monitoring: Datadog deployed across the new infrastructure for real-time visibility into application performance, infrastructure health, and streaming quality metrics capabilities the on-prem setup never had.
Implementation Process
- Discovery & Requirement Analysis (Weeks 1–3): Mapped the existing on-prem architecture in full, identified dependencies between services, and defined the migration sequencing to minimize risk.
- Prototype / PoC (Weeks 4–6): Migrated a single non-critical service (content metadata API) first to validate the migration approach and tooling before touching core systems.
- Development (Weeks 7–13): Built out the full AWS infrastructure via Terraform, migrated the media transcoding pipeline, and containerized application services.
- Integration (Weeks 14–16): Set up database replication for the live cutover, integrated Datadog monitoring, and configured CloudFront distribution.
- Testing (Weeks 17–18): Ran the new AWS infrastructure in parallel with the on-prem system, load-tested the auto-scaling configuration against simulated peak traffic.
- Deployment (Week 19): Executed the phased cutover, shifting traffic service by service with real-time monitoring at each stage and an immediate rollback path if issues arose.
- Optimization (Week 20 and ongoing): Tuned auto-scaling thresholds and CDN cache policies based on real traffic patterns observed post-migration.
Key Features Delivered
- Fully redundant, Multi-AZ cloud infrastructure replacing single-point-of-failure on-prem servers
- Global CDN-based content delivery via CloudFront
- Elastic, auto-scaling video transcoding pipeline using AWS MediaConvert
- Auto-scaling application tier handling traffic spikes without manual intervention
- Zero-downtime migration executed via phased, service-by-service cutover
- Infrastructure as Code covering 100% of the new cloud environment
- Real-time infrastructure and streaming quality monitoring via Datadog
Business Results
- Platform achieved zero subscriber-facing downtime throughout the entire 20-week migration
- Video transcoding time dropped from 6–8 hours per title to under 45 minutes
- Streaming latency for out-of-region subscribers improved significantly, measured via reduced buffering complaints and improved playback start times
- Infrastructure team shifted from reactive server maintenance to platform improvement work, with maintenance-related tickets dropping sharply
- The platform successfully handled a major content release traffic spike post-migration without degraded service a scenario that would have caused an outage on the old infrastructure
- Infrastructure costs became variable and traffic-aligned rather than fixed data-center overhead, improving cost efficiency during lower-traffic periods
Technology Stack
| Layer | Technology |
|---|---|
| Compute | AWS EC2 (auto-scaling), Docker |
| Content Delivery | AWS CloudFront |
| Media Processing | AWS MediaConvert, S3 |
| Database | AWS RDS (Multi-AZ), AWS DMS |
| Caching | AWS ElastiCache (Redis) |
| Infrastructure as Code | Terraform |
| CI/CD | GitHub Actions |
| Monitoring | Datadog |
Why the Solution Worked
The migration succeeded because it was executed as a phased, service-by-service transition rather than a risky single cutover. Running new AWS infrastructure in parallel with the existing on-prem system, and shifting traffic gradually with a clear rollback path at each stage, is what made a zero-downtime migration achievable for a live platform actively serving subscribers.
Rebuilding rather than simply lifting-and-shifting the transcoding pipeline was equally important replicating the old hardware-bound approach in the cloud would have carried the same scaling limitations forward. Modernizing the architecture where it mattered most delivered real operational improvement, not just a change of address for the same constraints.
Future Scalability
The new AWS foundation is built to support the client’s growth plans, including:
- Expanding CDN edge coverage as the subscriber base grows into new regions
- Adding a recommendation engine that leverages the now-centralized subscriber viewing data
- Building automated content quality checks into the transcoding pipeline
- Extending the auto-scaling infrastructure to support live-streaming events alongside on-demand content
Because the entire environment is defined in Terraform and containerized, scaling to new regions or adding new services extends the existing infrastructure rather than requiring another migration effort.
Final Outcome
The client moved from a fragile, single-location infrastructure with a real outage history to a redundant, globally distributed, auto-scaling cloud platform without a single minute of subscriber-facing downtime during the transition. The platform is now positioned to handle growth and traffic spikes that would have previously caused service failures.
