Compare Scenarios

Streaming Media Platform is the simpler architecture. Healthcare Records Platform carries lower operational risk. They share 7 component(s). Healthcare Records Platform has 6 unique risk(s); Streaming Media Platform has 6.

Limited confidence

Left

Healthcare Records Platform

expertPlatform Engineering Team

Nodes

Edges

Risks

Seeds

Strengths

Adv. Risks

Right

Streaming Media Platform

highExperienced Backend Team

Nodes

Edges

Risks

Seeds

Strengths

Adv. Risks

Comparison Dimensions

Complexity

Streaming Media Platformgiven this evidence

Healthcare Records Platform

expert complexity, 19 nodes, 0 edges, 6 risks, 3 simulation seeds

Streaming Media Platform

high complexity, 21 nodes, 0 edges, 6 risks, 3 simulation seeds

Streaming Media Platform is simpler: high operational complexity with 21 topology nodes vs 19 for Healthcare Records Platform.

Operational Risk

Healthcare Records Platformgiven this evidence

Healthcare Records Platform

6 risks (top: high), 4 high/critical, 1 confirmed by simulation

Streaming Media Platform

6 risks (top: high), 5 high/critical, 0 confirmed by simulation

Healthcare Records Platform has lower operational risk: weighted severity score 20 vs 22 (1 vs 0 simulation-confirmed).

Scalability

Healthcare Records Platformgiven this evidence

Healthcare Records Platform

4 scaling thresholds, 3 migration paths, 6 advisor scaling signals

Streaming Media Platform

4 scaling thresholds, 3 migration paths, 4 advisor scaling signals

Healthcare Records Platform has more defined scaling paths: 4 thresholds and 3 migration paths.

Operational Maturity

Tie

Healthcare Records Platform

Advisor assessment: Advanced; recommended team: Platform Engineering Team; 9 operational requirements

Streaming Media Platform

Advisor assessment: Advanced; recommended team: Experienced Backend Team; 14 operational requirements

Both scenarios require equivalent team maturity: Advanced.

Observability

Healthcare Records Platformgiven this evidence

Healthcare Records Platform

8 watched metrics, 5 observability recommendations, 3 simulation seeds

Streaming Media Platform

8 watched metrics, 7 observability recommendations, 3 simulation seeds

Healthcare Records Platform has lower observability burden: 8 watched metrics vs 8.

Generator Readiness

Depends

Healthcare Records Platform

generator relevance documented; topology generation relevance noted; simulation relevance noted; 3 seeds with generator notes

Streaming Media Platform

generator relevance documented; topology generation relevance noted; simulation relevance noted; 3 seeds with generator notes

Both scenarios have comparable generator readiness at this stage. Generator support is preliminary. Neither scenario should be treated as fully generation-ready.

Architecture Components

Shared (7)

Event Sourcing· architecture pattern Rate Limiting· architecture pattern Change Data Capture via WAL· architecture pattern Apache Kafka· event stream PostgreSQL· primary datastore Redis· cache Event Streaming· workload

Only in Healthcare Records Platform (12)

CQRS (Command Query Responsibility Segregation)· architecture pattern Index Table· architecture pattern Transactional Outbox Pattern· architecture pattern Read Replica· architecture patternConfiguration Drift· operational riskDeadlock· operational riskLock Contention· operational riskPartial Service Failure· operational riskReplication Lag Cascade· operational riskSchema Migration Lock· operational riskMixed OLTP (SaaS Core)· workload Write-Heavy Transactional· workload

Only in Streaming Media Platform (14)

Backpressure· architecture pattern Cache-Aside· architecture pattern Competing Consumers· architecture patternCascading Failure· operational riskDisk I/O Saturation· operational riskHot Partition· operational riskQueue Backlog Accumulation· operational riskSlow Consumer· operational riskThundering Herd (Cache Stampede)· operational riskApache Cassandra· primary datastore MinIO· supporting component Batch ETL Pipeline· workload Read-Heavy API Backend· workload Time-Series Metrics· workload

Operational Risks

Only in Healthcare Records Platform (6)

Configuration Drift· moderate Deadlock· high Lock Contention· high Partial Service Failure· moderate Replication Lag Cascade· moderate Schema Migration Lock· high

Only in Streaming Media Platform (6)

Cascading Failure· high Disk I/O Saturation· high Hot Partition· high Queue Backlog Accumulation· high Slow Consumer· moderate Thundering Herd (Cache Stampede)· high

Consistency Guarantees

Neither scenario has a recorded consistency-guarantee claim.

Neither scenario has recorded a consistency-guarantee claim; no guarantee comparison is possible from the data on record.

Tradeoff Summary

Complexity vs Risk

Healthcare Records Platform has expert complexity. Streaming Media Platform has high complexity. Simpler systems often carry different (not necessarily fewer) risks.

Healthcare Records Platform

Healthcare Records Platform: 6 risks (top: high), 4 high/critical, 1 confirmed by simulation

Streaming Media Platform

Streaming Media Platform: 6 risks (top: high), 5 high/critical, 0 confirmed by simulation

Scaling Path

Healthcare Records Platform offers 4 defined scaling thresholds. Streaming Media Platform offers 4. More defined paths means clearer evolution steps but also more anticipated growth.

Healthcare Records Platform

4 scaling thresholds, 3 migration paths, 6 advisor scaling signals

Streaming Media Platform

4 scaling thresholds, 3 migration paths, 4 advisor scaling signals

Team Maturity Requirement

Streaming Media Platform can be operated by a less experienced team. Healthcare Records Platform requires deeper operational expertise.

Healthcare Records Platform

Advisor assessment: Advanced; recommended team: Platform Engineering Team; 9 operational requirements

Streaming Media Platform

Advisor assessment: Advanced; recommended team: Experienced Backend Team; 14 operational requirements

Migration Considerations

Migration Step 1

Healthcare Records Platform

Mutable clinical records with application-layer audit logging → Event-sourced clinical records with atomic audit event + outbox writes

Streaming Media Platform

Synchronous transcoding in the upload request handler (blocking API response) → Async transcoding via Kafka topic with competing consumer workers

Both scenarios define a migration step at this stage. Healthcare Records Platform: triggered by 'HIPAA audit requirement exposed during external security rev'. Streaming Media Platform: triggered by 'Upload API p99 exceeding 30 seconds due to in-process transc'.

Migration Step 2

Healthcare Records Platform

Inline Kafka publish inside clinical transaction (dual-write) → Outbox pattern with CDC relay for FHIR event delivery

Streaming Media Platform

Viewing history in PostgreSQL → Viewing history in Cassandra

Both scenarios define a migration step at this stage. Healthcare Records Platform: triggered by 'FHIR events being published to Kafka but corresponding clini'. Streaming Media Platform: triggered by 'PostgreSQL viewing history table exceeding 500M rows; write '.

Migration Step 3

Healthcare Records Platform

All facilities sharing a single PostgreSQL cluster → Per-facility database with cross-facility patient index and record linkage

Streaming Media Platform

Single CDN provider with no origin rate limiting → Multi-CDN with origin request coalescing and rate limiting

Both scenarios define a migration step at this stage. Healthcare Records Platform: triggered by 'Facility acquisition or merger; compliance requirement for d'. Streaming Media Platform: triggered by 'CDN provider incident causing total origin failover; CDN mis'.

Advisor Notes

Healthcare Records Platform

Risk (high): Replication Lag Cascade

Asynchronous replicas fall behind the primary under write load and serve reads from an older version of the data. Reads keep succeeding, so nothing errors; what breaks is one of three specific consistency guarantees (read-after-write, monotonic reads, or consistent prefix), each with a distinct user-visible anomaly.

Streaming Media Platform

Risk (high): Queue Backlog Accumulation

Message queue or event stream consumer processing rate falls below producer write rate, causing consumer lag to grow unboundedly: eventually leading to increased end-to-end latency, producer backpressure, data expiry, or queue resource exhaustion.

Both

Shared Operational Requirements

Both scenarios require: Apache Kafka: scenario has team_maturity below senior, Apache Kafka: scenario uses Kafka for event streaming or CDC, Cache sizing and eviction policy configuration.

Supporting Evidence · 15 items

Scenario

healthcare_records_platformScenario 'Healthcare Records Platform' provides composition, operational complexity, scaling thresholds, migration paths, and team maturity requirements.

Scenario

streaming_media_platformScenario 'Streaming Media Platform' provides composition, operational complexity, scaling thresholds, migration paths, and team maturity requirements.

Topology

healthcare_records_platformTopology for 'healthcare_records_platform': 19 nodes, 0 edges, 6 risk nodes.

Topology

streaming_media_platformTopology for 'streaming_media_platform': 21 nodes, 0 edges, 6 risk nodes.

Risk Path

prop_architecture_pattern_read_replica_risk_replication_lag_cascadeRead Replica → Replication Lag Cascade

Risk Path

prop_workload_profile_write_heavy_transactional_risk_lock_contentionWrite-Heavy Transactional → Lock Contention

Risk Path

prop_workload_profile_event_streaming_workload_risk_queue_backlog_accumulationEvent Streaming → Queue Backlog Accumulation. also affects: Slow Consumer

Risk Path

prop_technology_profile_redis_risk_thundering_herdRedis → Thundering Herd (Cache Stampede)

Seed

healthcare_records_platform__replication_lag_cascade__replication_lagTests how Replication Lag Cascade manifests in Healthcare Records Platform under stress conditions. Involves 1 architecture component.

Seed

healthcare_records_platform__lock_contention__generic_risk_probeTests how Lock Contention manifests in Healthcare Records Platform under stress conditions. Involves 1 architecture component.

Seed

streaming_media_platform__queue_backlog_accumulation__queue_backlogTests how Queue Backlog Accumulation manifests in Streaming Media Platform under stress conditions. Involves 2 architecture components.

Seed

streaming_media_platform__thundering_herd__generic_risk_probeTests how Thundering Herd (Cache Stampede) manifests in Streaming Media Platform under stress conditions. Involves 1 architecture component.

Execution

healthcare_records_platform__replication_lag_cascade__replication_lag_executionReplication lag exceeds 5s threshold at peak: stale reads reach 28%

Advisor

advisor_healthcare_records_platformAdvisor for 'Healthcare Records Platform': 0 strengths, 6 risks, maturity: advanced.

Advisor

advisor_streaming_media_platformAdvisor for 'Streaming Media Platform': 0 strengths, 6 risks, maturity: advanced.

Coverage Warnings

⚠Healthcare Records Platform: fewer than 2 architectural strengths identified. the risk/complexity dimensions are present but the strength analysis is thin. Consider enriching the relationship YAMLs referenced by this scenario to improve coverage.
⚠Streaming Media Platform: fewer than 2 architectural strengths identified. the risk/complexity dimensions are present but the strength analysis is thin. Consider enriching the relationship YAMLs referenced by this scenario to improve coverage.

Limitations

·Comparison grounded in YAML knowledge only. Not measured from any production system.
·Winner assessments are deterministic heuristics, not absolute recommendations. Context, team preferences, and workload specifics may change the conclusion.
·5 seed type(s) across both scenarios do not have execution previews. Risk confirmation for those types is unavailable.
·Neither scenario has a recorded consistency-guarantee claim. Guarantee comparison is uninformative for this pair, not silently empty.

Final Architecture RecommendationPreliminary confidence

Healthcare Records Platform is the recommended starting point over Streaming Media Platform

Healthcare Records Platform leads on 3 weighted dimension(s): Operational Risk, Scalability, Observability. Weighted score: 5.0 vs 2.5 for Streaming Media Platform.

View Healthcare Records Platform →

Decision Intelligence

Architecture Decision Path

Structured reasoning for choosing between Healthcare Records Platform and Streaming Media Platform. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.

Healthcare Records Platform is the recommended starting point over Streaming Media Platform

Healthcare Records Platform leads on 3 weighted dimension(s): Operational Risk, Scalability, Observability. Weighted score: 5.0 vs 2.5 for Streaming Media Platform. The architectures share 7 component(s), reducing migration cost if you switch later. Streaming Media Platform is the operationally simpler choice.

Recommendation:Left

Confidence Preliminary

Where to Start

→

Start with Streaming Media Platform

Right

Streaming Media Platform has lower operational complexity. Starting here reduces risk and cognitive load. Migrate to the more capable architecture only when you hit concrete scaling or feature limits.

Complexity: high complexity, 21 nodes, 0 edges, 6 risks, 3 simulation seeds

Migrate when:

Kafka consumer group lag on the transcoding topic growing during peak upload hours; content availability delay > 10 minutes for newly uploaded videos; transcoding worker CPU consistently above 85% across all instances → Increase transcoding consumer instances up to the transcoding topic partition count; tune partition count to match the maximum desired worker parallelism (set this at topic creation, not after lag appears); implement per-uploader upload rate limits to smooth burst input; consider priority queuing so premium-tier content does not wait behind bulk ingest jobs
MinIO GET request rate spikes > 10x baseline immediately after content publish or CDN invalidation; MinIO p99 latency > 500ms; CDN miss ratio > 5% on popular content → Implement origin request coalescing (single origin fetch per CDN node per object, queue subsequent requestors for the in-flight response); pre-warm CDN edges for anticipated high-traffic content before publish; add rate limiting at the origin gateway to cap per-second origin requests per content_id
Cassandra node CPU imbalance > 40% across cluster; write latency p99 spiking on specific nodes; nodetool tpstats showing dropped mutations on hot nodes → Add a write_bucket component to the partition key (e.g., content_id + time bucket modulo N) to distribute writes across N partitions per content_id; tune N based on expected peak write rate per content item; read queries must fan out across all N buckets and merge, which increases read complexity but eliminates write hotspots

Decision Flow

Does your team have the operational maturity to run Healthcare Records Platform (advanced rating)?

If Yes

Your team can operate Healthcare Records Platform. Continue to Step 2 to refine based on risk tolerance and workload fit.

If No

Prefer the lower-maturity option: right scenario.

Right

Is operational stability and minimising production risk your primary concern over feature richness or scalability ceiling?

If Yes

Prefer Healthcare Records Platform: it carries lower operational risk weight per the advisor's assessment.

Left

If No

Proceed to Step 3 to evaluate based on scaling requirements.

Do you expect your load to reach: Audit log table growing at > 500K rows/day; INSERT p99 on audit_log > 20ms; autovacuum unable to keep up with dead tuple accumulation from UPDATE operations on the audit log's index pages ?

If Yes

Left scenario has more defined scaling evolution paths for this growth pattern.

Left

If No

If you don't expect to hit these scaling signals soon, prefer the simpler architecture and re-evaluate when load patterns become clearer.

Is operational simplicity (fewer moving parts, easier debugging, lower ops burden) more important than maximum architectural capability?

If Yes

Streaming Media Platform is the simpler choice: Streaming Media Platform is simpler: high operational complexity with 21 topology nodes vs 19 for Healthcare Records Platform.

Right

If No

If capability and scalability ceiling matter more than simplicity, evaluate the higher-complexity scenario against your specific load model.

When to Choose Each Scenario

Healthcare Records Platform

Left

When stability and predictability matter most

Critical

Healthcare Records Platform carries lower overall risk weight per the advisor's assessment.

When you need well-defined scaling thresholds and migration paths

High

Healthcare Records Platform has more documented scaling evolution steps (4 thresholds, 3 migration paths).

When you want to minimise monitoring setup overhead

Moderate

Healthcare Records Platform has a lower observability burden: fewer watched metrics and monitoring targets.

When your system requires decoupled async event processing

High

Healthcare Records Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.

Streaming Media Platform

Right

When operational simplicity is a top priority

High

Streaming Media Platform has lower operational complexity: fewer moving parts, easier to reason about and debug.

When your system requires decoupled async event processing

High

Streaming Media Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.

When to Avoid Each Scenario

Healthcare Records Platform

Left

When your team cannot mitigate: replication lag cascade

High

This architecture is significantly exposed to Replication Lag Cascade. Asynchronous replicas fall behind the primary under write load and serve reads from an older version of the data. Reads keep succeeding, so nothing errors; what breaks is one of three specific consistency guarantees (read-after-write, monotonic reads, or consistent prefix), each with a distinct user-visible anomaly.

When your team cannot mitigate: lock contention

High

This architecture is significantly exposed to Lock Contention. Concurrent writers to the same rows serialize behind each other's row locks, so latency is set not by the work a transaction does but by how long it waits for the writers ahead of it. On a hot row the queue depth, and therefore the tail latency, grows with concurrency while throughput flattens. Blocked writers hold connections open, so a single contended row can drain the connection pool as a secondary failure.

When your team is early-stage or solo

High

Healthcare Records Platform is rated 'advanced'. It requires experienced backend engineers or platform tooling to operate reliably at scale.

When you expect rapid growth within the next 12–18 months

Moderate

The advisor identifies 7 predicted bottlenecks for Healthcare Records Platform. Rapid growth will surface these limitations quickly.

Streaming Media Platform

Right

When your team cannot mitigate: queue backlog accumulation

High

This architecture is significantly exposed to Queue Backlog Accumulation. Message queue or event stream consumer processing rate falls below producer write rate, causing consumer lag to grow unboundedly: eventually leading to increased end-to-end latency, producer backpressure, data expiry, or queue resource exhaustion.

When your team cannot mitigate: thundering herd (cache stampede)

High

This architecture is significantly exposed to Thundering Herd (Cache Stampede). When a popular cached key expires or a service recovers from downtime, all requests that were waiting or arrive simultaneously miss the cache and hit the origin database concurrently, producing a request spike that can overwhelm the database within seconds.

When your team is early-stage or solo

High

Streaming Media Platform is rated 'advanced'. It requires experienced backend engineers or platform tooling to operate reliably at scale.

When you expect rapid growth within the next 12–18 months

Moderate

The advisor identifies 9 predicted bottlenecks for Streaming Media Platform. Rapid growth will surface these limitations quickly.

Team Fit

Solo developer or small startup

Left

Healthcare Records Platform is more accessible for small teams. Fewer operational moving parts reduces on-call burden.

↳Validate that the simpler architecture can handle your projected load before committing.

Small product team (2–6 engineers)

Left

Healthcare Records Platform suits small teams that need to move fast without deep platform tooling investment.

↳Consider Streaming Media Platform only if your workload pattern specifically requires it.

Experienced backend team

Depends

An experienced team can operate either architecture. Choose based on workload fit, not team capability.

↳Prioritise alignment with existing infrastructure and tooling.
↳Streaming Media Platform may require additional runbook coverage and alerting investment.

Platform engineering team or SRE-equipped organisation

Right

A platform team can safely operate Streaming Media Platform and will benefit from its more advanced scaling characteristics.

↳Ensure observability and alerting are configured before launch.

Migration Triggers

Left→RightPlan

Migration Step 1

Left→RightPlan

Migration Step 2

Left→RightPlan

Migration Step 3

Left→DependsAct Soon

Audit log table growing at > 500K rows/day; INSERT p99 on audit_log > 20ms; autovacuum unable to keep up with dead tuple accumulation from UPDATE operations on the audit log's index pages

Tier 1: Audit Log Write Throughput: Audit log receiving one row per record access creates I/O contention with clinical record writes on the same PostgreSQL primary. Recommended evolution: Partition the audit_log table by month using PostgreSQL declarative partitioning; child partitions allow VACUUM to operate on bounded table segments without scanning the entire history; index each partition independently to keep index size proportional to partition row count rather than total log size .

Left→DependsAct Soon

pg_locks showing RowExclusiveLock waits on clinical_records or encounter_notes during shift-change peak hours; write p99 > 100ms; occasional deadlock errors in application logs correlated with concurrent addenda writes to the same encounter

Tier 2: Concurrent Encounter Write Lock Contention: Multiple clinical staff members writing addenda to the same encounter simultaneously, or two processes updating encounter status concurrently. Recommended evolution: Implement optimistic locking with an encounter version column; reject concurrent writes with a conflict error and require the client to reload and retry; this eliminates lock waits by failing fast rather than waiting; ensure the application presents a clear conflict resolution UI: in a clinical context, silent overwrites of concurrent edits are a patient safety risk, not just a data integrity issue .

Right→DependsAct Soon

Kafka consumer group lag on the transcoding topic growing during peak upload hours; content availability delay > 10 minutes for newly uploaded videos; transcoding worker CPU consistently above 85% across all instances

Tier 1: Transcoding Worker Throughput: Transcoding consumer group undersized relative to peak upload volume. Recommended evolution: Increase transcoding consumer instances up to the transcoding topic partition count; tune partition count to match the maximum desired worker parallelism (set this at topic creation, not after lag appears); implement per-uploader upload rate limits to smooth burst input; consider priority queuing so premium-tier content does not wait behind bulk ingest jobs .

Right→DependsAct Soon

MinIO GET request rate spikes > 10x baseline immediately after content publish or CDN invalidation; MinIO p99 latency > 500ms; CDN miss ratio > 5% on popular content

Tier 2: CDN Origin Thundering Herd: CDN cache miss storm on first-play of new or recently-updated content. Recommended evolution: Implement origin request coalescing (single origin fetch per CDN node per object, queue subsequent requestors for the in-flight response); pre-warm CDN edges for anticipated high-traffic content before publish; add rate limiting at the origin gateway to cap per-second origin requests per content_id .

Readiness Requirements

Apache Kafka: scenario has team_maturity below senior

Both

Kafka operational complexity requires dedicated expertise: consider MSK or Confluent Cloud to reduce ops burden

Required maturity: senior

Apache Kafka: scenario uses Kafka for event streaming or CDC

Both

Set min.insync.replicas=2 with acks=all; monitor consumer lag as primary health signal

Required maturity: senior

Cache sizing and eviction policy configuration

Both

Redis or equivalent cache requires correct maxmemory configuration, eviction policy selection (allkeys-lru is common), and cold-start warming strategy after restarts.

Event stream operations expertise

Both

This architecture includes event stream infrastructure (Kafka, Kinesis, or similar). Operations requires consumer group management, partition assignment, dead-letter handling, and lag monitoring.

Required maturity: platform_engineering_team

PostgreSQL: scenario includes high_write_throughput or write_heavy workload

Both

Deploy PgBouncer in transaction-mode pooling before relying on vertical scaling

Required maturity: mid_level

Redis: scenario has read_heavy workload with high cache miss risk

Both

Implement cache stampede protection (probabilistic early expiry or locking) to prevent thundering herd on cold start

Required maturity: junior

Redis: scenario relies on Redis for data that cannot be re-derived

Both

Redis is not a durable store: add persistence layer or treat Redis as expendable cache only

Required maturity: junior

Runbooks and alerting for high-severity risks

Both

4 high-severity risks identified. Each requires a documented runbook, alerting threshold, and on-call response procedure before running in production.

Minimum team maturity: Platform Engineering Team

Left

This scenario has expert operational complexity. It is recommended for Platform Engineering Team teams or higher.

Required maturity: platform_engineering_team

Apache Cassandra: scenario has team_maturity below staff_plus

Right

Cassandra has the highest operational complexity of common datastores: consider managed options (Astra DB, Keyspaces) or simpler alternatives

Required maturity: staff_plus

Apache Cassandra: scenario has time_series or iot_telemetry workload

Right

Design partition keys with time-bucketing (e.g., date prefix) to prevent wide partitions as data grows

Required maturity: staff_plus

Apache Cassandra: scenario requires ad-hoc queries or analytics

Right

Cassandra cannot efficiently query non-partition-key dimensions: pair with Elasticsearch or ClickHouse for analytics

Required maturity: staff_plus

MinIO: scenario enables versioning without lifecycle expiration policies

Right

Configure ILM lifecycle policies with expiration rules for versioned objects; without expiration, version accumulation on high-churn objects consumes storage unboundedly

Required maturity: mid_level

MinIO: scenario stores large numbers of small objects (< 100KB average size)

Right

MinIO's per-request overhead reduces effective throughput for small objects; evaluate aggregating small objects into larger archives or using a key-value store for small object access patterns

Required maturity: mid_level

Minimum team maturity: Experienced Backend Team

Right

This scenario has high operational complexity. It is recommended for Experienced Backend Team teams or higher.

Required maturity: experienced_backend_team

Generator Constraints

Healthcare Records Platform

Left

Generator relevance documented but not yet production-ready.

For healthcare or compliance-heavy product briefs requiring full audit trails, the generator must output event sourcing + atomic audit log writes + outbox pattern as mandatory structural components, not optional enhancements. PostgreSQL RLS policy templates targeting patient-identifiable tables must be generated as non-optional. The generator must surface synchronous replication configuration (synchronous_commit setting and standby count) as an explicit output with a note about the per-write latency tradeoff.

Streaming Media Platform

Right

Generator relevance documented but not yet production-ready.

For content platform briefs with video or audio delivery requirements, the generator should output the Kafka async transcoding pipeline, MinIO object storage, and CDN-first delivery as the canonical composition. Redis playback session with TTL enforcement and Cassandra for time-ordered viewing history should be generated as separate store responsibilities. The generator must flag the partition key design decision for the Cassandra history table as a mandatory architecture decision requiring explicit access pattern enumeration before schema creation.

Supporting Evidence

Type	Reference	Explanation
Comparison	`compare_healthcare_records_platform_vs_streaming_media_platform`	Full comparison of Healthcare Records Platform vs Streaming Media Platform: 6 dimensions, 7 shared components, 0 shared risks.
Advisor	`advisor_healthcare_records_platform`	Advisor for Healthcare Records Platform: 0 strengths, 6 risks, maturity: advanced.
Advisor	`advisor_streaming_media_platform`	Advisor for Streaming Media Platform: 0 strengths, 6 risks, maturity: advanced.
Scenario	`healthcare_records_platform`	Scenario 'Healthcare Records Platform': 4 scaling thresholds, 3 migration paths, complexity: expert.
Scenario	`streaming_media_platform`	Scenario 'Streaming Media Platform': 4 scaling thresholds, 3 migration paths, complexity: high.
Risk Path	`prop_architecture_pattern_read_replica_risk_replication_lag_cascade`	Read Replica → Replication Lag Cascade
Risk Path	`prop_workload_profile_write_heavy_transactional_risk_lock_contention`	Write-Heavy Transactional → Lock Contention
Risk Path	`prop_workload_profile_event_streaming_workload_risk_queue_backlog_accumulation`	Event Streaming → Queue Backlog Accumulation. also affects: Slow Consumer
Risk Path	`prop_technology_profile_redis_risk_thundering_herd`	Redis → Thundering Herd (Cache Stampede)
Risk Path	`prop_architecture_pattern_read_replica_risk_replication_lag_cascade`	Referenced by the operational risk comparison dimension.
Risk Path	`prop_workload_profile_write_heavy_transactional_risk_lock_contention`	Referenced by the operational risk comparison dimension.

Limitations

·Decision guidance is grounded in YAML knowledge only. Not measured from any production system.
·Recommendations are deterministic heuristics based on structured knowledge. Your specific workload, team profile, and business context may lead to different conclusions.
·Generator constraints are preliminary. No scenario should be treated as production generation-ready at this stage.

Comparison complete

Profile, topology, simulation, advisor, comparison, and decision path are ready. Your architecture decision is grounded in structured knowledge and deterministic reasoning.

View Healthcare Records Platform →View Streaming Media Platform →Compare another pair →Review a new system profile →