Compare Scenarios
Side-by-side comparison with decision path analysis. Every dimension traces back to topology, risk propagation, simulation, and advisor intelligence.
Select Scenarios to Compare
Left Scenario
AI / RAG
Multi-Tenant SaaS
Analytics
Financial Ledger
Read-Heavy
Multi-Tenant SaaS
Write-Heavy
Marketplace
Event-Driven
Financial Ledger
Realtime Collab
Realtime Collab
Financial Ledger
Write-Heavy
AI / RAG
Multi-Tenant SaaS
Event-Driven
Analytics
Read-Heavy
Realtime Collab
Search-Heavy
Event-Driven
Event-Driven
Marketplace
Write-Heavy
Right Scenario
AI / RAG
Multi-Tenant SaaS
Analytics
Financial Ledger
Read-Heavy
Multi-Tenant SaaS
Write-Heavy
Marketplace
Event-Driven
Financial Ledger
Realtime Collab
Realtime Collab
Financial Ledger
Write-Heavy
AI / RAG
Multi-Tenant SaaS
Event-Driven
Analytics
Read-Heavy
Realtime Collab
Search-Heavy
Event-Driven
Event-Driven
Marketplace
Write-Heavy
Topology at a Glance
Architecture Comparison
IoT Telemetry Ingestion Platform vs Streaming Media Platform: IoT Telemetry Ingestion Platform is the simpler choice
IoT Telemetry Ingestion Platform is the simpler architecture. They share 13 component(s). IoT Telemetry Ingestion Platform has 2 unique risk(s); Streaming Media Platform has 2.
19
Nodes
0
Edges
6
Risks
3
Seeds
0
Strengths
6
Adv. Risks
21
Nodes
0
Edges
6
Risks
3
Seeds
0
Strengths
6
Adv. Risks
Comparison Dimensions
Complexity
IoT Telemetry Ingestion Platform
high complexity, 19 nodes, 0 edges, 6 risks, 3 simulation seeds
Streaming Media Platform
high complexity, 21 nodes, 0 edges, 6 risks, 3 simulation seeds
IoT Telemetry Ingestion Platform is simpler: high operational complexity with 19 topology nodes vs 21 for Streaming Media Platform.
Operational Risk
IoT Telemetry Ingestion Platform
6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Streaming Media Platform
6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Both scenarios carry equivalent risk weight (22). Neither is meaningfully safer at this granularity.
Scalability
IoT Telemetry Ingestion Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Streaming Media Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Both scenarios have comparable scaling paths. The best choice depends on your specific growth trajectory. IoT Telemetry Ingestion Platform and Streaming Media Platform offer similar numbers of defined evolution steps.
Operational Maturity
IoT Telemetry Ingestion Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 12 operational requirements
Streaming Media Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 14 operational requirements
Both scenarios require equivalent team maturity: Advanced.
Observability
IoT Telemetry Ingestion Platform
8 watched metrics, 7 observability recommendations, 3 simulation seeds
Streaming Media Platform
8 watched metrics, 7 observability recommendations, 3 simulation seeds
Both scenarios have similar observability requirements: 8 and 8 watched metrics respectively.
Generator Readiness
IoT Telemetry Ingestion 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 (13)
Only in IoT Telemetry Ingestion Platform (6)
Only in Streaming Media Platform (8)
Operational Risks
Shared (4)
Only in IoT Telemetry Ingestion Platform (2)
Only in Streaming Media Platform (2)
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
IoT Telemetry Ingestion Platform has high complexity. Streaming Media Platform has high complexity. Simpler systems often carry different (not necessarily fewer) risks.
IoT Telemetry Ingestion Platform
IoT Telemetry Ingestion Platform: 6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Streaming Media Platform
Streaming Media Platform: 6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Scaling Path
IoT Telemetry Ingestion Platform offers 4 defined scaling thresholds. Streaming Media Platform offers 4. More defined paths means clearer evolution steps but also more anticipated growth.
IoT Telemetry Ingestion Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Streaming Media Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Migration Considerations
Migration Step 1
IoT Telemetry Ingestion Platform
Direct device writes to PostgreSQL with time-range partitioning → Kafka ingestion buffer + TimescaleDB consumer writers
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. IoT Telemetry Ingestion Platform: triggered by 'PostgreSQL write p99 > 100ms at sustained device fleet load;'. Streaming Media Platform: triggered by 'Upload API p99 exceeding 30 seconds due to in-process transc'.
Migration Step 2
IoT Telemetry Ingestion Platform
TimescaleDB as sole query layer for both real-time and historical queries → Redis last-known-value cache for real-time queries + TimescaleDB for historical queries
Streaming Media Platform
Viewing history in PostgreSQL → Viewing history in Cassandra
Both scenarios define a migration step at this stage. IoT Telemetry Ingestion Platform: triggered by 'Alerting system query latency > 500ms due to TimescaleDB que'. Streaming Media Platform: triggered by 'PostgreSQL viewing history table exceeding 500M rows; write '.
Migration Step 3
IoT Telemetry Ingestion Platform
TimescaleDB for both ingest storage and analytics queries → TimescaleDB for hot storage + ClickHouse for fleet analytics
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. IoT Telemetry Ingestion Platform: triggered by 'Multi-device aggregate queries (fleet-wide max/min/avg over '. Streaming Media Platform: triggered by 'CDN provider incident causing total origin failover; CDN mis'.
Advisor Notes
Risk (high): Hot Partition
One partition (a database shard, a Kafka topic partition, a Redis hash slot) receives traffic so far above its peers that it saturates while the others sit idle. Aggregate capacity looks healthy, but the hot partition throttles or lags, and everything routed to it degrades. The cause is skew in how keys map to partitions, and the fix depends on whether the skew is spread across many keys or concentrated in one.
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.
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 · 14 items
Coverage Warnings
- ⚠IoT Telemetry Ingestion 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.
- ·6 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.
IoT Telemetry Ingestion Platform is the recommended starting point over Streaming Media Platform
IoT Telemetry Ingestion Platform leads on 1 weighted dimension(s): Complexity. Weighted score: 4.0 vs 2.5 for Streaming Media Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between IoT Telemetry Ingestion Platform and Streaming Media Platform. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.
IoT Telemetry Ingestion Platform is the recommended starting point over Streaming Media Platform
IoT Telemetry Ingestion Platform leads on 1 weighted dimension(s): Complexity. Weighted score: 4.0 vs 2.5 for Streaming Media Platform. The architectures share 13 component(s), reducing migration cost if you switch later. IoT Telemetry Ingestion Platform is the operationally simpler choice.
Where to Start
Start with IoT Telemetry Ingestion Platform
LeftIoT Telemetry Ingestion 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, 19 nodes, 0 edges, 6 risks, 3 simulation seeds
Migrate when:
- TimescaleDB write latency p99 > 50ms for batch INSERT operations; pg_stat_activity showing wait events on WAL flush; TimescaleDB active chunk autovacuum running continuously; Kafka consumer group lag for storage writers growing steadily at baseline (non-storm) load → Tune TimescaleDB chunk_time_interval to match write cadence (smaller chunks = faster compression, lower WAL amplification per chunk); enable native compression on chunks older than 1 hour to reduce on-disk footprint; add a dedicated NVMe volume for WAL separate from data directory; consider TimescaleDB multi-node for horizontal write distribution across data nodes
- Kafka consumer group lag jumping from baseline (<100k) to >10M messages within minutes; Kafka broker disk write rate elevated; TimescaleDB write thread pool fully saturated; Redis last-known-value update latency acceptable but historical storage significantly behind real-time; device reconnect event visible in device authentication logs correlating with lag spike → Pre-scale storage writer consumer replicas before anticipated high-risk windows (maintenance events, regional failovers); implement burst-aware consumer scaling using consumer group lag as the autoscale signal; tune Kafka consumer max.poll.records to batch storage INSERTs into TimescaleDB for higher per-consumer throughput (target 500–1000 rows per INSERT batch rather than single-row inserts)
- TimescaleDB I/O saturation visible in disk throughput metrics during specific consumer lag drain periods; chunk decompression operations appearing in TimescaleDB logs (decompress_chunk); write latency spiking for historical time ranges (not current time chunk); device backlog replay operations (devices offline >1 hour) correlating with I/O spikes → Implement a late-data ingest path separate from the real-time ingest path: late data (> 2 hours old by device timestamp) routes to a dedicated consumer that writes to a separate TimescaleDB hypertable with relaxed compression policy; this isolates late-data decompression I/O from the real-time write path; add monitoring alert when device timestamp delta vs. wall clock > 2 hours
Decision Flow
Does your team have the operational maturity to run IoT Telemetry Ingestion Platform (advanced rating)?
If Yes
Your team can operate IoT Telemetry Ingestion Platform. Continue to Step 2 to refine based on risk tolerance and workload fit.
If No
Prefer the lower-maturity option: right scenario.
Is operational stability and minimising production risk your primary concern over feature richness or scalability ceiling?
If Yes
Both scenarios carry similar risk weight. Continue to Step 3.
If No
Proceed to Step 3 to evaluate based on scaling requirements.
Do you expect your load to reach: high sustained load with clear migration paths?
If Yes
Both scenarios have comparable scaling paths. Choose based on complexity preference.
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
IoT Telemetry Ingestion Platform is the simpler choice: IoT Telemetry Ingestion Platform is simpler: high operational complexity with 19 topology nodes vs 21 for Streaming Media Platform.
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
IoT Telemetry Ingestion Platform
LeftWhen operational simplicity is a top priority
HighIoT Telemetry Ingestion Platform has lower operational complexity: fewer moving parts, easier to reason about and debug.
When your system requires decoupled async event processing
HighIoT Telemetry Ingestion Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.
Streaming Media Platform
RightWhen your system requires decoupled async event processing
HighStreaming Media Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.
When to Avoid Each Scenario
IoT Telemetry Ingestion Platform
LeftWhen your team cannot mitigate: hot partition
HighThis architecture is significantly exposed to Hot Partition. One partition (a database shard, a Kafka topic partition, a Redis hash slot) receives traffic so far above its peers that it saturates while the others sit idle. Aggregate capacity looks healthy, but the hot partition throttles or lags, and everything routed to it degrades. The cause is skew in how keys map to partitions, and the fix depends on whether the skew is spread across many keys or concentrated in one.
When your team cannot mitigate: write amplification cascade
HighThis architecture is significantly exposed to Write Amplification Cascade. Each logical application write triggers multiple physical writes through index maintenance, WAL generation, MVCC versioning, and replication, causing actual disk IOPS to exceed the provisioned I/O ceiling while the logical write rate appears modest.
When your team is early-stage or solo
HighIoT Telemetry Ingestion 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
ModerateThe advisor identifies 9 predicted bottlenecks for IoT Telemetry Ingestion Platform. Rapid growth will surface these limitations quickly.
Streaming Media Platform
RightWhen your team cannot mitigate: queue backlog accumulation
HighThis 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)
HighThis 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
HighStreaming 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
ModerateThe advisor identifies 9 predicted bottlenecks for Streaming Media Platform. Rapid growth will surface these limitations quickly.
Team Fit
Solo developer or small startup
LeftIoT Telemetry Ingestion 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)
LeftIoT Telemetry Ingestion 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
DependsAn 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
RightA 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
Migration Step 1
Both scenarios define a migration step at this stage. IoT Telemetry Ingestion Platform: triggered by 'PostgreSQL write p99 > 100ms at sustained device fleet load;'. Streaming Media Platform: triggered by 'Upload API p99 exceeding 30 seconds due to in-process transc'.
Migration Step 2
Both scenarios define a migration step at this stage. IoT Telemetry Ingestion Platform: triggered by 'Alerting system query latency > 500ms due to TimescaleDB que'. Streaming Media Platform: triggered by 'PostgreSQL viewing history table exceeding 500M rows; write '.
Migration Step 3
Both scenarios define a migration step at this stage. IoT Telemetry Ingestion Platform: triggered by 'Multi-device aggregate queries (fleet-wide max/min/avg over '. Streaming Media Platform: triggered by 'CDN provider incident causing total origin failover; CDN mis'.
TimescaleDB write latency p99 > 50ms for batch INSERT operations; pg_stat_activity showing wait events on WAL flush; TimescaleDB active chunk autovacuum running continuously; Kafka consumer group lag for storage writers growing steadily at baseline (non-storm) load
Tier 1: TimescaleDB Write Throughput Ceiling: TimescaleDB single-node write throughput ceiling (~50k–100k rows/second depending on row width and chunk size configuration). Recommended evolution: Tune TimescaleDB chunk_time_interval to match write cadence (smaller chunks = faster compression, lower WAL amplification per chunk); enable native compression on chunks older than 1 hour to reduce on-disk footprint; add a dedicated NVMe volume for WAL separate from data directory; consider TimescaleDB multi-node for horizontal write distribution across data nodes .
Kafka consumer group lag jumping from baseline (<100k) to >10M messages within minutes; Kafka broker disk write rate elevated; TimescaleDB write thread pool fully saturated; Redis last-known-value update latency acceptable but historical storage significantly behind real-time; device reconnect event visible in device authentication logs correlating with lag spike
Tier 2: Kafka Consumer Lag from Reconnect Storm: Kafka consumer pool sized for steady-state throughput, not burst from device reconnect storm; insufficient storage writer parallelism for burst absorption. Recommended evolution: Pre-scale storage writer consumer replicas before anticipated high-risk windows (maintenance events, regional failovers); implement burst-aware consumer scaling using consumer group lag as the autoscale signal; tune Kafka consumer max.poll.records to batch storage INSERTs into TimescaleDB for higher per-consumer throughput (target 500–1000 rows per INSERT batch rather than single-row inserts) .
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 .
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
BothKafka 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
BothSet min.insync.replicas=2 with acks=all; monitor consumer lag as primary health signal
Required maturity: senior
Cache sizing and eviction policy configuration
BothRedis 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
BothThis 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
Minimum team maturity: Experienced Backend Team
BothThis scenario has high operational complexity. It is recommended for Experienced Backend Team teams or higher.
Required maturity: experienced_backend_team
PostgreSQL: scenario includes high_write_throughput or write_heavy workload
BothDeploy 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
BothImplement 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
BothRedis 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
Both5 high-severity risks identified. Each requires a documented runbook, alerting threshold, and on-call response procedure before running in production.
ClickHouse: scenario has analytics_olap or event_aggregation workload
LeftBatch inserts to ClickHouse in minimum 1k-row batches; single-row inserts cause part fragmentation
Required maturity: mid_level
ClickHouse: scenario uses ClickHouse for OLTP workloads
LeftClickHouse is an OLAP engine: mutations (UPDATE/DELETE) are async and expensive; use PostgreSQL for transactional workloads
Required maturity: mid_level
TimescaleDB: scenario requires real-time aggregation rollups at high insert rates
LeftConfigure continuous aggregates with appropriate refresh intervals; do not use caggs for sub-second freshness requirements: use a streaming aggregation layer instead
Required maturity: mid_level
Apache Cassandra: scenario has team_maturity below staff_plus
RightCassandra 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
RightDesign 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
RightCassandra 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
RightConfigure 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)
RightMinIO'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
Generator Constraints
IoT Telemetry Ingestion Platform
LeftGenerator relevance documented but not yet production-ready.
For IoT product briefs, the generator must produce the three-tier ingest architecture: device endpoint → Kafka → (TimescaleDB writer + Redis state writer). The continuous aggregate view configuration (1m/1h/1d rollups with explicit refresh policy) must be generated as part of the TimescaleDB schema. Redis key TTL calculation from device reporting interval must be generated as a first-class configuration parameter. The late-arriving data routing path must be generated with a device timestamp delta threshold as a configurable constant, not hardcoded.
Streaming Media Platform
RightGenerator 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_iot_telemetry_ingestion_vs_streaming_media_platform | Full comparison of IoT Telemetry Ingestion Platform vs Streaming Media Platform: 6 dimensions, 13 shared components, 4 shared risks. |
| Advisor | advisor_iot_telemetry_ingestion | Advisor for IoT Telemetry Ingestion Platform: 0 strengths, 6 risks, maturity: advanced. |
| Advisor | advisor_streaming_media_platform | Advisor for Streaming Media Platform: 0 strengths, 6 risks, maturity: advanced. |
| Scenario | iot_telemetry_ingestion | Scenario 'IoT Telemetry Ingestion Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| Scenario | streaming_media_platform | Scenario 'Streaming Media Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| Risk Path | prop_workload_profile_write_heavy_transactional_risk_wal_saturation | Write-Heavy Transactional → WAL Saturation |
| Risk Path | prop_workload_profile_time_series_metrics_risk_disk_io_saturation | Time-Series Metrics → Disk I/O Saturation |
| 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_workload_profile_write_heavy_transactional_risk_wal_saturation | Referenced by the operational risk comparison dimension. |
| Risk Path | prop_workload_profile_time_series_metrics_risk_disk_io_saturation | 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.