MicrocosmWorksื—ื“ืฉื ื•ืช ื•ืชื›ื ื•ืŸ ืงื•ืกืžื•ืก ื“ื™ื’ื™ื˜ืœื™
ืื•ื“ื•ืชืฆื•ืจ ืงืฉืจ
MicrocosmWorksืžื—ื“ืฉื™ื ื•ืžืชื›ื ื ื™ื ืงื•ืกืžื•ืก ื“ื™ื’ื™ื˜ืœื™

ืžืกืคืงื™ื ืคืชืจื•ื ื•ืช IT ื—ืฉื•ื‘ื™ื. ืื ื• ื ืœื”ื‘ื™ื ืžื˜ื›ื ื•ืœื•ื’ื™ื”, ืื‘ื˜ื—ื” ื•ืขื•ื–ืจื™ื ืœืขืกืงื™ื ืœืฆืžื•ื— ื‘ืืžืฆืขื•ืช ืชืฉืชื™ืช IT ืืžื™ื ื” ื•ื—ื“ืฉื ื™ืช.

[email protected]
+91 7011868196
New Delhi, India

ืžืจื›ื– ืฆืžื™ื—ื” AI

ืžืจื›ื– AIื—ื“ืฉื ื•ืช ืกื˜ืืจื˜ืืคืžืื™ืฅ ืืจื’ื•ื ื™

ืคืชืจื•ื ื•ืช

ื›ืœ ื”ืคืชืจื•ื ื•ืชืืคืœื™ืงืฆื™ื•ืช ื‘ืจื™ืื•ืช ื•ื›ื•ืฉืจืคืœื˜ืคื•ืจืžืช ื•ื™ื“ืื• AIืคื™ืชื•ื— ืกื•ื›ื ื™ AI

ืžืฉืื‘ื™ื

ืชื•ื‘ื ื•ืชืžื“ืจื™ื›ื™ ืชืขืฉื™ื™ื”ืชื•ื›ื ื™ื•ืช ืžืงืจื” ืฉื™ืžื•ืฉืชื‘ื ื™ื•ืช ืืจื›ื™ื˜ืงื˜ื•ืจื”ืžื—ืงืจื™ ืžืงืจื”

ื—ื‘ืจื”

ืื•ื“ื•ืชื™ื ื•ืฆื•ืจ ืงืฉืจื”ืขื‘ื•ื“ื” ืฉืœื ื•

ืฉื™ืจื•ืชื™ื

ื™ื™ืขื•ืฅ ื“ื™ื’ื™ื˜ืœื™ืชืฉืชื™ืช ืขื ืŸืคื™ืชื•ื— SaaSืคื™ืชื•ื— AIื˜ื›ื ื•ืœื•ื’ื™ื™ืช ื•ื™ื“ืื•
ืคื™ืชื•ื— ERPื”ืชืืžื” ืื™ืฉื™ืช ืฉืœ Zohoืคื™ืชื•ื— Odooืื™ื ื˜ื’ืจืฆื™ื” ืฉืœ Salesforceืคื™ืชื•ื— CRM ืžื•ืชืื ืื™ืฉื™ืช
ืื™ื ื˜ื’ืจืฆื™ื” ืฉืœ QuickBooksืคืชืจื•ื ื•ืช IoTืคื™ืชื•ื— ื‘ืœื•ืงืฆ'ื™ื™ืŸ
ื™ื™ืขื•ืฅ ืกื™ื™ื‘ืจืชืžื™ื›ื” ื˜ื›ื ื™ืช - L3

ยฉ 2026 MicrocosmWorks. ื›ืœ ื”ื–ื›ื•ื™ื•ืช ืฉืžื•ืจื•ืช.

ืžื“ื™ื ื™ื•ืช ืคืจื˜ื™ื•ืชืชื ืื™ ืฉื™ืจื•ืช
ื—ื–ืจื” ืœืชื‘ื ื™ื•ืช ืืจื›ื™ื˜ืงื˜ื•ืจื”
DataEnterprise

ืžืขืจื›ื•ืช ืกื˜ืจื™ืžื™ื ื’ ื‘ื–ืžืŸ ืืžืช

ืขื™ื‘ื•ื“ ืืฆื•ื•ื” (Batch) ื”ื•ื ืžืงืจื” ืคืจื˜ื™ ืฉืœ ืกื˜ืจื™ืžื™ื ื’. ื›ืืฉืจ ื”ืขืกืง ืฉืœืš ืฆืจื™ืš ืœื”ื’ื™ื‘ ืชื•ืš ืฉื ื™ื•ืช ื‘ืžืงื•ื ืฉืขื•ืช, ืืชื” ื–ืงื•ืง ืœืืจื›ื™ื˜ืงื˜ื•ืจื” ื”ื‘ื ื•ื™ื” ืœื–ืจื™ืžืช ื ืชื•ื ื™ื ืจืฆื™ืคื”.

June 18, 2026
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3 topics covered
ื“ื™ื•ืŸ ื‘ืืจื›ื™ื˜ืงื˜ื•ืจื” ื–ื•
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Enterprise
Complexity
Financial Services, Logistics
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Technologies

ืžืชื™ ื–ื” ื ื—ื•ืฅ ืœืš

ื”ื“ืืฉื‘ื•ืจื“ื™ื ืฉืœืš ืื™ื ื ืขื“ื›ื ื™ื™ื ื‘ืจื’ืข ืฉืžื™ืฉื”ื• ืžืกืชื›ืœ ืขืœื™ื”ื. ื–ื™ื”ื•ื™ ื”ื•ื ืื•ืช ืžืชื‘ืฆืข ื›ืขื‘ื•ื“ืช ืืฆื•ื•ื” ืœื™ืœื™ืช, ื•ืชื•ืคืก ื”ื•ื ืื•ืช ืจืง ืœืžื—ืจืช ื‘ื‘ื•ืงืจ. ืกืคื™ืจื•ืช ืžืœืื™ ืžืชืขื“ื›ื ื•ืช ืžื“ื™ ืฉืขื”, ืžื” ืฉื’ื•ืจื ืœืžื›ื™ืจืช ื™ืชืจ. ื ืชื•ื ื™ ื—ื™ื™ืฉื ื™ื ื ืืกืคื™ื ืืš ืœื ืžื˜ื•ืคืœื™ื ืขื“ ืฉื”ื ืžื ื•ืชื—ื™ื ื‘ืชื”ืœื™ืš ETL ืœื™ืœื™. ืืชื” ื–ืงื•ืง ืœืžืขืจื›ืช ืฉื‘ื” ื ืชื•ื ื™ื ื–ื•ืจืžื™ื ื‘ืื•ืคืŸ ืจืฆื™ืฃ ืžืžืงื•ืจื•ืช, ื“ืจืš ืขื™ื‘ื•ื“, ืืœ ืฆืจื›ื ื™ื, ืขื ื”ืฉื”ื™ื” ืฉืœ ืคื—ื•ืช ืžืฉื ื™ื™ื” โ€“ ืื ืœื™ื˜ื™ืงื” ื‘ื–ืžืŸ ืืžืช, ื”ืชืจืื•ืช ื—ื™ื•ืช, ื”ืกืงืช ืžืกืงื ื•ืช (inference) ืฉืœ AI ื‘ืกื˜ืจื™ืžื™ื ื’, ื•ืกื ื›ืจื•ืŸ ืžื™ื™ื“ื™ ื‘ื™ืŸ ืžืขืจื›ื•ืช.

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ืืจื›ื™ื˜ืงื˜ื•ืจืช ืคืœื˜ืคื•ืจืžื” ืขืชื™ืจืช ื ืชื•ื ื™ื

ื›ืืฉืจ ื”ื™ืชืจื•ืŸ ื”ืชื—ืจื•ืชื™ ืฉืœื›ื ื˜ืžื•ืŸ ื‘ื ืชื•ื ื™ื ืฉืœื›ื, ื”ืคืœื˜ืคื•ืจืžื” ืฉืื•ืกืคืช, ื”ื•ืคื›ืช, ืžืื—ืกื ืช ื•ืžืฆื™ื’ื” ื ืชื•ื ื™ื ืืœื• ื”ื™ื ื”ื“ื‘ืจ ื”ื—ืฉื•ื‘ ื‘ื™ื•ืชืจ ืฉืชื‘ื ื•.

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ื”ืื ืืชื” ื–ืงื•ืง ืœืขื–ืจื” ื‘ื”ื˜ืžืขืช ืืจื›ื™ื˜ืงื˜ื•ืจื” ื–ื•?

ืื“ืจื™ื›ืœื™ื ืฉืœื ื• ื™ื›ื•ืœื™ื ืœืขื–ื•ืจ ืœืš ืœืขืฆื‘ ื•ืœื‘ื ื•ืช ืžืขืจื›ื•ืช ืชื•ืš ืฉื™ืžื•ืฉ ื‘ื“ืคื•ืก ื–ื” ืœื“ืจื™ืฉื•ืช ื”ืกืคืฆื™ืคื™ื•ืช ืฉืœืš.

ืฆืจื• ืงืฉืจ

ืกืงื™ืจืช ื“ืคื•ืก

ืืจื›ื™ื˜ืงื˜ื•ืจืช ืกื˜ืจื™ืžื™ื ื’ ื‘ื–ืžืŸ ืืžืช ืžืขื‘ื“ืช ื ืชื•ื ื™ื ื›ื–ืจื™ืžื” ืจืฆื™ืคื” ื•ื‘ืœืชื™ ืžื•ื’ื‘ืœืช ื•ืœื ื›ืืฆื•ื•ืช ื ืคืจื“ื•ืช. ืžืคื™ืงื™ ืื™ืจื•ืขื™ื ืžืคืจืกืžื™ื ืœืคืœื˜ืคื•ืจืžืช ืกื˜ืจื™ืžื™ื ื’ (Kafka, Kinesis, Pulsar). ืžืขื‘ื“ื™ ืกื˜ืจื™ืžื™ื ื’ (Flink, Kafka Streams, custom consumers) ืžืฉื ื™ื, ืžืขืฉื™ืจื™ื, ืžืกื ื ื™ื ื•ืžืื’ื“ื™ื ืื™ืจื•ืขื™ื ืชื•ืš ื›ื“ื™ ืชื ื•ืขื”. ืชื•ืฆืื•ืช ืžืขื•ื‘ื“ื•ืช ื ื“ื—ืคื•ืช ืœืฆืจื›ื ื™ื: ื“ืืฉื‘ื•ืจื“ื™ื ื‘ื–ืžืŸ ืืžืช (WebSocket), ืื™ื ื“ืงืกื™ ื—ื™ืคื•ืฉ (Elasticsearch), ืžืกื“ื™ ื ืชื•ื ื™ื ืื ืœื™ื˜ื™ื™ื (ClickHouse), ื•ืฉื™ืจื•ืชื™ื ื‘ืžื•ืจื“ ื”ื–ืจื (downstream services). Change Data Capture (CDC) ืžืืคืฉืจ ืœืžืกื“ื™ ื ืชื•ื ื™ื ืงื™ื™ืžื™ื ืœื”ืฉืชืชืฃ ื›ืžืงื•ืจื•ืช ืื™ืจื•ืขื™ื ืœืœื ืฉื™ื ื•ื™ื™ื ื‘ื™ื™ืฉื•ื.

ืืจื›ื™ื˜ืงื˜ื•ืจืช ื™ื™ื—ื•ืก

ืœืืจื›ื™ื˜ืงื˜ื•ืจื” ื™ืฉ ืืจื‘ืข ืฉื›ื‘ื•ืช. ืžืงื•ืจื•ืช ืื™ืจื•ืขื™ื ืžื™ื™ืฆืจื™ื ื ืชื•ื ื™ื โ€“ ืื™ืจื•ืขื™ ื™ื™ืฉื•ืžื™ื, ื–ืจืžื™ CDC ืฉืœ ืžืกื“ื™ ื ืชื•ื ื™ื, ื˜ืœืžื˜ืจื™ื” ืฉืœ IoT, clickstreams ืฉืœ ืžืฉืชืžืฉื™ื, ื•-webhooks ืฉืœ API ื—ื™ืฆื•ื ื™ื™ื. ืคืœื˜ืคื•ืจืžืช ื”ืกื˜ืจื™ืžื™ื ื’ (Kafka) ืžืกืคืงืช ืื—ืกื•ืŸ ืื™ืจื•ืขื™ื ืขืžื™ื“, ืžืกื•ื“ืจ ื•ื ื™ืชืŸ ืœืฉื—ื–ื•ืจ. ืžืขื‘ื“ื™ ืกื˜ืจื™ืžื™ื ื’ ืฆื•ืจื›ื™ื ืžื ื•ืฉืื™ื (topics), ืžื™ื™ืฉืžื™ื ื˜ืจื ืกืคื•ืจืžืฆื™ื•ืช (ืกื™ื ื•ืŸ, ื”ืขืฉืจื”, ืฆื‘ื™ืจื” ืžื‘ื•ืกืกืช ื—ืœื•ื ื•ืช, ืฆื™ืจื•ืคื™ื), ื•ืžืคื™ืงื™ื ืœ-topics ืื• sinks ืคืœื˜. ืฆืจื›ื ื™ื ืžื ื•ื™ื™ื ืœื–ืจืžื™ื ืžืขื•ื‘ื“ื™ื โ€“ ืฉืจืชื™ WebSocket ื“ื•ื—ืคื™ื ืœื“ืคื“ืคื ื™ื, ืžื—ื‘ืจื™ื (connectors) ืฉื•ืงืขื™ื ืœืžืกื“ื™ ื ืชื•ื ื™ื, ืžื ื•ืขื™ ื”ืชืจืื” ืžืขืจื™ื›ื™ื ื›ืœืœื™ื ื•ืžื•ืฆื™ืื™ื ื”ืชืจืื•ืช.

ืจื›ื™ื‘ื™ ืœื™ื‘ื”
  • ืคืœื˜ืคื•ืจืžืช ืกื˜ืจื™ืžื™ื ื’ (Kafka): ืืฉื›ื•ืœ ืžืจื•ื‘ื” Brokerื™ื ืขื ืืจื’ื•ืŸ ืฉืœ Topic ืœื›ืœ ืกื•ื’ ืื™ืจื•ืข. ืžื—ื•ืœืง ืœืžื—ื™ืฆื•ืช ืœืฆื•ืจืš ืžืงื‘ื™ืœื™ื•ืช (partition key = entity ID ืœื”ื‘ื˜ื—ืช ืกื“ืจ). ืฉืžื™ืจื” ืžื•ื’ื“ืจืช ืœื›ืœ Topic โ€“ 7 ื™ืžื™ื ืœืื™ืจื•ืขื™ื ืชืคืขื•ืœื™ื™ื, 30+ ื™ืžื™ื ืœื‘ื™ืงื•ืจืช/ืฉื—ื–ื•ืจ. Schema Registry (Confluent ืื• Apicurio) ืื•ื›ืฃ ืชืื™ืžื•ืช ืกื›ื™ืžืช ืื™ืจื•ืขื™ื ื‘ื™ืŸ ืžืคื™ืงื™ื ื•ืฆืจื›ื ื™ื
  • Change Data Capture (CDC): ืžื—ื‘ืจื™ Debezium ืœื•ื›ื“ื™ื ืฉื™ื ื•ื™ื™ื ื‘ืจืžืช ื”ืฉื•ืจื” ืž-PostgreSQL, MySQL, ืื• MongoDB ื•ืžืคืจืกืžื™ื ืื•ืชื ื›ืื™ืจื•ืขื™ื ืœ-Kafka. ื–ื” ื”ื•ืคืš ืžืกื“ื™ ื ืชื•ื ื™ื ืงื™ื™ืžื™ื ืœืžืงื•ืจื•ืช ืื™ืจื•ืขื™ื ืžื‘ืœื™ ืœืฉื ื•ืช ืงื•ื“ ื™ื™ืฉื•ื โ€“ ื—ื™ื•ื ื™ ืœื”ื’ื™ืจื” ื”ื“ืจื’ืชื™ืช ืœืืจื›ื™ื˜ืงื˜ื•ืจื•ืช ืžื•ื ื—ื•ืช ืื™ืจื•ืขื™ื
  • ืžื ื•ืข ืขื™ื‘ื•ื“ ืกื˜ืจื™ืžื™ื ื’: Apache Flink ืœืขื™ื‘ื•ื“ ืื™ืจื•ืขื™ื ืžื•ืจื›ื‘ โ€“ ืฆื‘ื™ืจื” ืžื‘ื•ืกืกืช ื—ืœื•ื ื•ืช, ืฆื™ืจื•ืคื™ Stream-Stream, ื–ื™ื”ื•ื™ ืชื‘ื ื™ื•ืช. Kafka Streams ืœื˜ืจื ืกืคื•ืจืžืฆื™ื•ืช ืคืฉื•ื˜ื•ืช ื™ื•ืชืจ ืฉืื™ื ืŸ ื“ื•ืจืฉื•ืช ืืฉื›ื•ืœ ืขื™ื‘ื•ื“ ื ืคืจื“. ืฆืจื›ื ื™ื ืžื•ืชืืžื™ื ืื™ืฉื™ืช ื‘-Node.js/Python ืœื˜ื™ืคื•ืœ ืงืœ ืžืฉืงืœ ื‘ืื™ืจื•ืขื™ื
  • ืžืกื™ืจื” ื‘ื–ืžืŸ ืืžืช: ืฉืจืช WebSocket (Socket.io, WS ืžืงื•ืจื™) ืœื“ื—ื™ืคืช ืขื“ื›ื•ื ื™ื ื—ื™ื™ื ืœืœืงื•ื—ื•ืช ื“ืคื“ืคืŸ. Server-Sent Events (SSE) ืœืกื˜ืจื™ืžื™ื ื’ ื—ื“-ื›ื™ื•ื•ื ื™. GraphQL Subscriptions ืขื‘ื•ืจ ืฉืื™ืœืชื•ืช ื‘ื–ืžืŸ ืืžืช ื‘ื˜ื•ื—ื•ืช-ืกื•ื’ (type-safe). ืืจื›ื™ื˜ืงื˜ื•ืจืช Fan-out ื”ืžืคืจื™ื“ื” ืืช ืชืคื•ืงืช ื”ืžืคื™ืง ืžืžืกืคืจ ื—ื™ื‘ื•ืจื™ ื”ืฆืจื›ืŸ

ื”ื—ืœื˜ื•ืช ืชื›ื ื•ืŸ ื•ืคืฉืจื•ืช

Kafka ืœืขื•ืžืช Kinesis ืœืขื•ืžืช Pulsar
Kafka ืขื‘ื•ืจ ืฆื•ื•ืชื™ื ื”ื–ืงื•ืงื™ื ืœืžืขืจื›ืช ืืงื•ืœื•ื’ื™ืช ื‘ืฉืœื” ื‘ื™ื•ืชืจ, ืชืคื•ืงื” ื’ื‘ื•ื”ื” ื‘ื™ื•ืชืจ, ื•ืฉืœื™ื˜ื” ืžืœืื” (ื‘ื ื™ื”ื•ืœ ืขืฆืžื™ ืื• Confluent Cloud). Kinesis ืขื‘ื•ืจ ืฆื•ื•ืชื™ื ืฉื•ืจืฉื™ AWS ื”ืžืขื•ื ื™ื™ื ื™ื ื‘ืืคืก ื ื˜ืœ ืชืคืขื•ืœื™ ืขื ื“ืจื™ืฉื•ืช ืชืคื•ืงื” ื ืžื•ื›ื•ืช ื™ื•ืชืจ. Pulsar ืœืกื˜ืจื™ืžื™ื ื’ ืžืจื•ื‘ื” ื“ื™ื™ืจื™ื (multi-tenant) ืขื ืื—ืกื•ืŸ ืžื“ื•ืจื’ ืžื•ื‘ื ื” ื•ืฉื›ืคื•ืœ ื’ืื•ื’ืจืคื™ (geo-replication). MW ื‘ืจื™ืจืช ื”ืžื—ื“ืœ ื”ื™ื Kafka (MSK ืื• Confluent Cloud) ืขื‘ื•ืจ ืจื•ื‘ ืืจื›ื™ื˜ืงื˜ื•ืจื•ืช ื”ืกื˜ืจื™ืžื™ื ื’ โ€“ ื”ืžืขืจื›ืช ื”ืืงื•ืœื•ื’ื™ืช ืฉืœ connectors, ื›ืœื™ ืขื‘ื•ื“ื” ื•ื™ื“ืข ืชืคืขื•ืœื™ ื”ื™ื ืœืœื ืชื—ืจื•ืช.
Flink ืœืขื•ืžืช Kafka Streams ืœืขื•ืžืช Custom Consumers
Flink ืขื‘ื•ืจ ืœื•ื’ื™ืงืช ืกื˜ืจื™ืžื™ื ื’ ืžื•ืจื›ื‘ืช โ€“ ืฆื‘ื™ืจื” ืžื‘ื•ืกืกืช ื—ืœื•ื ื•ืช, ืฆื™ืจื•ืคื™ Stream, CEP (ืขื™ื‘ื•ื“ ืื™ืจื•ืขื™ื ืžื•ืจื›ื‘), ืกืžื ื˜ื™ืงื” ืฉืœ Exactly-Once. Kafka Streams ื›ืืฉืจ ื”ืขื™ื‘ื•ื“ ืคืฉื•ื˜ ื™ื•ืชืจ ื•ืืชื” ืจื•ืฆื” ืœื”ื™ืžื ืข ืžื”ืคืขืœืช ืืฉื›ื•ืœ Flink ื ืคืจื“. ืฆืจื›ื ื™ื ืžื•ืชืืžื™ื ืื™ืฉื™ืช (Node.js, Python) ืœื˜ื™ืคื•ืœ ืคืฉื•ื˜ ื‘ืื™ืจื•ืขื™ื ืฉืื™ื ื• ื“ื•ืจืฉ ืคืจื™ืžื™ื˜ื™ื‘ื™ื ืฉืœ ืขื™ื‘ื•ื“ ืกื˜ืจื™ืžื™ื ื’. MW ืžืฉืชืžืฉืช ื‘-Flink ืขื‘ื•ืจ Pipelines ืขืชื™ืจื™ ืื ืœื™ื˜ื™ืงื” ื•ื‘-Kafka Streams ืื• ืฆืจื›ื ื™ื ืžื•ืชืืžื™ื ืื™ืฉื™ืช ืœืชืงืฉื•ืจืช Microservice ืžื•ื ื—ืช ืื™ืจื•ืขื™ื.
Exactly-Once ืœืขื•ืžืช At-Least-Once
ืกืžื ื˜ื™ืงื” ืฉืœ Exactly-Once (ื˜ืจื ื–ืงืฆื™ื•ืช Kafka + Flink checkpointing) ืžื‘ื˜ื™ื—ื” ืœืœื ื›ืคื™ืœื•ื™ื•ืช ืืš ืžื•ืกื™ืคื” ื”ืฉื”ื™ื” ื•ืžื•ืจื›ื‘ื•ืช. At-Least-Once ืขื ืฆืจื›ื ื™ื ืื™ื“ืžืคื•ื˜ื ื˜ื™ื™ื (idempotent) ืคืฉื•ื˜ื” ื™ื•ืชืจ ื•ืžืกืคืงืช ืœืจื•ื‘ ื”ืžืงืจื™ื โ€“ ืื ืขื™ื‘ื•ื“ ืื•ืชื• ืื™ืจื•ืข ืคืขืžื™ื™ื ืžืคื™ืง ืืช ืื•ืชื” ืชื•ืฆืื”, ืื™ื ืš ื–ืงื•ืง ืœ-Exactly-Once. MW ื‘ืจื™ืจืช ื”ืžื—ื“ืœ ื”ื™ื At-Least-Once ืขื Handlers ืื™ื“ืžืคื•ื˜ื ื˜ื™ื™ื ื•ืฉื•ืžืจืช ืืช Exactly-Once ืขื‘ื•ืจ ืขืกืงืื•ืช ืคื™ื ื ืกื™ื•ืช ื•ืื™ืจื•ืขื™ ื—ื™ื•ื‘ ืฉื‘ื”ื ืœื›ืคื™ืœื•ื™ื•ืช ื™ืฉ ื”ืฉืคืขื” ื›ืกืคื™ืช.
ืงื ื” ืžื™ื“ื” ืฉืœ WebSocket
ื›ืœ ื—ื™ื‘ื•ืจ WebSocket ืžื—ื–ื™ืง ื—ื™ื‘ื•ืจ TCP ืžืชืžืฉืš, ืžื” ืฉืžื’ื‘ื™ืœ ืืช ืžืกืคืจ ื”ืœืงื•ื—ื•ืช ืฉืฉืจืช ื™ื—ื™ื“ ื™ื›ื•ืœ ืœื˜ืคืœ ื‘ื”ื (~50K-100K ื—ื™ื‘ื•ืจื™ื ืœืฉืจืช). MW ืž ะผะฐััˆั‚ะฐื‘ืช ืืช ืืกืคืงืช WebSocket ื‘ืืžืฆืขื•ืช: (ื) ืืจื›ื™ื˜ืงื˜ื•ืจืช Fan-out ืฉื‘ื” ืฆืจื›ื ื™ Kafka ื“ื•ื—ืคื™ื ืœืฉื›ื‘ืช Redis Pub/Sub ืฉืžืคื™ืฆื” ืœืžืกืคืจ ืฉืจืชื™ WebSocket, (ื‘) ืงื ื” ืžื™ื“ื” ืื•ืคืงื™ (horizontal scaling) ืขื sticky sessions ืœื—ื™ื‘ื•ืจ ืžื—ื“ืฉ, ื•-(ื’) ื”ืฉืคืœื” ื”ื“ืจื’ืชื™ืช (graceful degradation) ืœ-polling ืขื‘ื•ืจ ืœืงื•ื—ื•ืช ื”ื ืžืฆืื™ื ืžืื—ื•ืจื™ ื—ื•ืžื•ืช ืืฉ ืžื’ื‘ื™ืœื•ืช.

ื‘ื—ื™ืจื•ืช ื˜ื›ื ื•ืœื•ื’ื™ื•ืช

ืฉื›ื‘ื”ื˜ื›ื ื•ืœื•ื’ื™ื•ืช
ืกื˜ืจื™ืžื™ื ื’Apache Kafka (MSK, Confluent), Kinesis, Apache Pulsar, Redpanda
CDCDebezium, AWS DMS, Maxwell
ืขื™ื‘ื•ื“Apache Flink, Kafka Streams, Benthos, ืฆืจื›ื ื™ื ืžื•ืชืืžื™ื ืื™ืฉื™ืช
ืžืกื™ืจื” ื‘ื–ืžืŸ ืืžืชWebSocket (Socket.io), SSE, GraphQL Subscriptions
ืื ืœื™ื˜ื™ืงื”ClickHouse, Apache Druid, Elasticsearch, TimescaleDB
ื ืจืื•ืช (Observability)Kafka lag monitoring (Burrow), Flink metrics, ืžืขืงื‘ ืื—ืจ ื”ืฉื”ื™ื” ืžื•ืชืื ืื™ืฉื™ืช

ืžืชื™ ืœื”ืฉืชืžืฉ / ืžืชื™ ืœื”ื™ืžื ืข

ื”ืฉืชืžืฉ ื›ืฉ-ื”ื™ืžื ืข ื›ืฉ-
ื”ื—ืœื˜ื•ืช ืขืกืงื™ื•ืช ื“ื•ืจืฉื•ืช ืจืขื ื ื•ืช ื ืชื•ื ื™ื ืฉืœ ืคื—ื•ืช ืžืฉื ื™ื™ื” (ื”ื•ื ืื”, ื ื™ื˜ื•ืจ, ืžืกื—ืจ)ืขื™ื‘ื•ื“ ืืฆื•ื•ื” ืขื ืจืขื ื ื•ืช ืฉืขืชื™ืช/ื™ื•ืžื™ืช ืขื•ื ื” ืขืœ ื”ืฆื•ืจืš ื”ืขืกืงื™
ืฆืจื›ื ื™ื ืžืจื•ื‘ื™ื ื–ืงื•ืงื™ื ืœืื•ืชื• ื–ืจื ืื™ืจื•ืขื™ื (Fan-out, ืžืขืจื›ื•ืช ืžื•ืคืจื“ื•ืช)ื™ืฉ ืœืš ืžืคื™ืง ื™ื—ื™ื“ ื•ืฆืจื›ืŸ ื™ื—ื™ื“ โ€“ ืชื•ืจ ืคืฉื•ื˜ ืžืกืคื™ืง
ืืชื” ื–ืงื•ืง ืœืฉื—ื–ื•ืจ ืื™ืจื•ืขื™ื ืœื ื™ืคื•ื™ ื‘ืื’ื™ื (debugging), ืขื™ื‘ื•ื“ ืžื—ื“ืฉ, ืื• ื‘ื ื™ื™ืช ืฆืจื›ื ื™ื ื—ื“ืฉื™ืื ืคื— ื”ื ืชื•ื ื™ื ื ืžื•ืš (< 1K events/min) ื•ืื™ื ื• ืžืฆื“ื™ืง ืชืฉืชื™ืช ืกื˜ืจื™ืžื™ื ื’
CDC ื ื—ื•ืฅ ืœืกื ื›ืจื•ืŸ ืžืกื“ื™ ื ืชื•ื ื™ื ืงื™ื™ืžื™ื ืœืžืขืจื›ื•ืช ื‘ืžื•ืจื“ ื”ื–ืจื (downstream systems) ืœืœื ืฉื™ื ื•ื™ื™ ืงื•ื“ืœืฆื•ื•ืช ื—ืกืจ ื ื™ืกื™ื•ืŸ ืขื ืžืขืจื›ื•ืช ืžื‘ื•ื–ืจื•ืช โ€“ ืกื˜ืจื™ืžื™ื ื’ ืžื•ืกื™ืฃ ืžื•ืจื›ื‘ื•ืช ืชืคืขื•ืœื™ืช ืžืฉืžืขื•ืชื™ืช

ื”ื’ื™ืฉื” ืฉืœื ื•

MW ืžืชื›ื ื ืช ืžืขืจื›ื•ืช ืกื˜ืจื™ืžื™ื ื’ ืขื "ืขืงืจื•ืŸ ื”ืฉื—ื–ื•ืจ" (replay principle) โ€“ ื›ืœ ื–ืจื ืฆืจื™ืš ืœื”ื™ื•ืช ื ื™ืชืŸ ืœืฉื—ื–ื•ืจ ืžื ืงื•ื“ืช ื–ืžืŸ ืžืกื•ื™ืžืช, ืžื” ืฉืžืืคืฉืจ ืœืฆืจื›ื ื™ื ื—ื“ืฉื™ื ืœืžืœื ื ืชื•ื ื™ื ื”ื™ืกื˜ื•ืจื™ื™ื ื•ืœืฆืจื›ื ื™ื ืงื™ื™ืžื™ื ืœืขื‘ื“ ืžื—ื“ืฉ ืœืื—ืจ ืชื™ืงื•ื ื™ ื‘ืื’ื™ื. ืคืจื™ืกื•ืช ื”-Kafka ืฉืœื ื• ื›ื•ืœืœื•ืช ืžื“ื™ื ื™ื•ืช ืื‘ื•ืœื•ืฆื™ื™ืช ืกื›ื™ืžื” (ืชื•ืืžืช ืœืื—ื•ืจ ื›ื‘ืจื™ืจืช ืžื—ื“ืœ), ื”ืชืจืื•ืช ืขืœ ืขื™ื›ื•ื‘ ืฆืจื›ื ื™ื (ืœืคื ื™ ืฉื”ื•ื ื”ื•ืคืš ืœืขื™ื›ื•ื‘ ื’ืœื•ื™ ืœืขืกืง), ื•-dead-letter topics ืขื ื ื™ืกื™ื•ืŸ ื—ื•ื–ืจ ืื•ื˜ื•ืžื˜ื™. ื‘ื ื™ื ื• Pipelines ืฉืœ ืกื˜ืจื™ืžื™ื ื’ ื”ืžืขื‘ื“ื™ื ืœืžืขืœื” ืž-500K ืื™ืจื•ืขื™ื ืœืฉื ื™ื™ื” ืขื‘ื•ืจ ืื ืœื™ื˜ื™ืงืช ื•ื™ื“ืื•, ื˜ืœืžื˜ืจื™ื” ืฉืœ IoT ื•ื“ืืฉื‘ื•ืจื“ื™ื ื‘ื–ืžืŸ ืืžืช.

ืชื‘ื ื™ื•ืช ืงืฉื•ืจื•ืช

  • ืžืขืจื›ืช ืžืขืงื‘ ื•ื™ื“ืื• AI ื‘ื–ืžืŸ ืืžืช โ€” ืกื˜ืจื™ืžื™ื ื’ ืฉืœ ืื™ืจื•ืขื™ ื•ื™ื“ืื• ื—ื™ ืขื ื”ืกืงืช ืžืกืงื ื•ืช (inference) ื‘ื–ืžืŸ ืืžืช
  • ืžื—ื•ืœืœ ืจื’ืขื™ ืฉื™ื ืฉืœ ืกืคื•ืจื˜ ื—ื™ โ€” ื–ื™ื”ื•ื™ ืื™ืจื•ืขื™ื ื‘ื–ืžืŸ ืืžืช ื•ื—ื™ืœื•ืฅ ืจื’ืขื™ ืฉื™ื
  • ืžืขืจื›ืช ืœื ื™ื”ื•ืœ ืฆื™ ืžื—ื•ื‘ืจ โ€” ืกื˜ืจื™ืžื™ื ื’ ืฉืœ ื˜ืœืžื˜ืจื™ื™ืช ืจื›ื‘ ืขื Geofencing
  • ืคืœื˜ืคื•ืจืžืช ื ืจืื•ืช ืฉืจืฉืจืช ืืกืคืงื” โ€” ืžืขืงื‘ ืื™ืจื•ืขื™ ืฉืจืฉืจืช ืืกืคืงื” ื‘ื–ืžืŸ ืืžืช

ืžืงืจื™ ื‘ื•ื—ืŸ ืงืฉื•ืจื™ื

  • ืžืขืงื‘ AI โ€” ืกื˜ืจื™ืžื™ื ื’ RTSP โ€” ืขื™ื‘ื•ื“ ื–ืจื ื•ื™ื“ืื• RTSP ื‘ื–ืžืŸ ืืžืช ืขื ื–ื™ื”ื•ื™ ืื™ืจื•ืขื™ื
  • ื ื™ืชื•ื— ื•ื™ื“ืื• โ€” ืื ืœื™ื˜ื™ืงืช ื•ื™ื“ืื• ื—ื™ื” ืขื Pipelines ืฉืœ ื”ืกืงืช ืžืกืงื ื•ืช ื‘ืกื˜ืจื™ืžื™ื ื’
  • ืงื™ื“ื•ื“ ื•ื™ื“ืื• โ€” ืชืฉืชื™ืช ืกื˜ืจื™ืžื™ื ื’ AWS Fast Channel HLS/SRT
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ืชืฉืชื™ืช ืฉืžื ื•ื”ืœืช ื‘ื’ืจืกืื•ืช, ื ื‘ื“ืงืช ื•ื ืคืจืกืช ื›ืžื• ืงื•ื“ ื™ื™ืฉื•ื โ€” ื›ื™ ื”ืคืœื˜ืคื•ืจืžื” ืฉืœืš ืืžื™ื ื” ืจืง ื›ืžื• ืžื” ืฉื ืžืฆื ืžืชื—ืชื™ื”.

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ืฉืืœื•ืช ื ืคื•ืฆื•ืช

MicrocosmWorks ืžืžืœื™ืฆื” ืขืœ Kafka ืœืฆื•ื•ืชื™ื ื”ื–ืงื•ืงื™ื ืœืฉื™ื“ื•ืจ ื—ื•ื–ืจ ืœืฆืจื›ื ื™ื ืžืจื•ื‘ื™ื (multi-consumer replay), ืชืงื•ืคื•ืช ืฉืžื™ืจื” ืืจื•ื›ื•ืช ื•ื ื™ื™ื“ื•ืช ื‘ื™ืŸ ืขื ื ื™ื (cross-cloud portability), ืฉื›ืŸ ื”ืืจื›ื™ื˜ืงื˜ื•ืจื” ืžื‘ื•ืกืกืช ื”ื™ื•ืžืŸ (log-based) ืฉืœื” ืชื•ืžื›ืช ื‘ืงื‘ื•ืฆื•ืช ืฆืจื›ื ื™ื ื‘ืœืชื™ ืžื•ื’ื‘ืœื•ืช ื”ืงื•ืจืื•ืช ืžื—ื“ืฉ ืืช ืื•ืชื• ื–ืจื ื ืชื•ื ื™ื ื‘ืื•ืคืŸ ืขืฆืžืื™. Kinesis ื”ื™ื ื”ื‘ื—ื™ืจื” ื”ื˜ื•ื‘ื” ื™ื•ืชืจ ื›ืืฉืจ ืืชื ืจื•ืฆื™ื ืฉื™ืจื•ืช ืžื ื•ื”ืœ ื‘ืžืœื•ืื• ื”ืžืฉืชืœื‘ ื”ื™ื˜ื‘ ืขื ืžืขืจื›ืช ื”ืืงื•ืœื•ื’ื™ืช ืฉืœ AWS ื•ืฆื•ืจื›ื™ ืฉืžื™ืจืช ื”ื ืชื•ื ื™ื ืฉืœื›ื ื”ื ืคื—ื•ืช ืž-7 ื™ืžื™ื ืขื ืคื—ื•ืช ืž-10 ื™ื™ืฉื•ืžื™ ืฆืจื›ืŸ. ืื ื• ืžืขืจื™ื›ื™ื ืืช ื”ื“ืจื™ืฉื•ืช ื”ืกืคืฆื™ืคื™ื•ืช ืฉืœื›ื โ€“ ืชืคื•ืงื” (throughput), ืฉืžื™ืจื” (retention), ื“ืคื•ืกื™ ืฆืจื›ื ื™ื (consumer patterns) ื•ื‘ื’ืจื•ืช ืชืคืขื•ืœื™ืช (operational maturity) โ€“ ื‘ืžื”ืœืš ื”ืขืจื›ืช ื”ืืจื›ื™ื˜ืงื˜ื•ืจื” ืฉืœื ื• ื›ื“ื™ ืœืชืช ืืช ื”ื”ืžืœืฆื” ื”ื ื›ื•ื ื”.

MicrocosmWorks ืžื™ื™ืฉืžืช ืกืžื ื˜ื™ืงื” ืฉืœ "ื‘ื“ื™ื•ืง ืคืขื ืื—ืช" (exactly-once semantics) ื‘ืืžืฆืขื•ืช ืฉื™ืœื•ื‘ ืฉืœ ืžืคื™ืงื™ื ืื™ื“ืžืคื•ื˜ื ื˜ื™ื™ื (idempotent producers), ืฆืจื›ื ื™ื ื˜ืจื ื–ืงืฆื™ื•ื ืœื™ื™ื ื•ืฉื›ื‘ื•ืช ื”ืกืจืช ื›ืคื™ืœื•ื™ื•ืช (deduplication layers) ื”ืžืฉืชืžืฉื•ืช ื‘ื˜ื‘ื™ืขื•ืช ืืฆื‘ืข ืฉืœ ืื™ืจื•ืขื™ื ื”ืžืื•ื—ืกื ื•ืช ื‘ืžื˜ืžื•ืŸ ื—ื™ืคื•ืฉ ืžื”ื™ืจ ื›ืžื• Redis. ืขื‘ื•ืจ ืžืขืจื›ื•ืช ืžื‘ื•ืกืกื•ืช Kafka, ืื ื• ืžื ืฆืœื™ื ืืช ื”-API ื”ื˜ืจื ื–ืงืฆื™ื•ื ืœื™ ื”ืžื•ื‘ื ื” ืฉืœ Kafka ื”ืžื‘ืฆืข ืื™ืฉื•ืจ ืื˜ื•ืžื™ ืฉืœ ืงื™ื–ื•ื–ื™ ืฆืจื›ื ื™ื ื•ื›ืชื™ื‘ื•ืช ืžืคื™ืงื™ื, ื‘ืขื•ื“ ืฉืขื‘ื•ืจ ืฆื™ื ื•ืจื•ืช ืกื˜ืจื™ืžื™ื ื’ ืžื•ืชืืžื™ื ืื™ืฉื™ืช ืื ื• ืžื™ื™ืฉืžื™ื ืืช ืชื‘ื ื™ืช ื”-"ืชื™ื‘ืช ื™ืฆื™ืื”" (outbox pattern) ืขื ื”ืกืจืช ื›ืคื™ืœื•ื™ื•ืช ืืฆืœ ื”ืฆืจื›ืŸ. ืื ื• ืชืžื™ื“ ืžืชื›ื ื ื™ื ืฆืจื›ื ื™ื ืœื”ื™ื•ืช ืื™ื“ืžืคื•ื˜ื ื˜ื™ื™ื ื›ืจืฉืช ื‘ื™ื˜ื—ื•ืŸ, ื›ืš ืฉื’ื ืื ืžื ื’ื ื•ืŸ ื”-"ื‘ื“ื™ื•ืง ืคืขื ืื—ืช" ื ื›ืฉืœ ื‘ืžืงืจื” ืงืฆื”, ืขื™ื‘ื•ื“ ืžื—ื“ืฉ ืฉืœ ืื™ืจื•ืข ืžืคื™ืง ืืช ืื•ืชื” ืชื•ืฆืื”.

MicrocosmWorks ืžืกืคืงืช ื‘ื“ืจืš ื›ืœืœ ื—ื‘ื™ื•ืŸ ืžืงืฆื” ืœืงืฆื” ืฉืœ 50-200ms ืขื‘ื•ืจ ืฆื™ื ื•ืจื•ืช ืกื˜ืจื™ืžื™ื ื’ ื”ื›ื•ืœืœื™ื ืงืœื™ื˜ื” (ingestion), ืขื™ื‘ื•ื“ ื•ื›ืชื™ื‘ื” ืœื›ื™ื•ืจ (sink writing), ื›ืืฉืจ ื ื™ืชืŸ ืœื”ืฉื™ื’ ื—ื‘ื™ื•ืŸ ืฉืœ ืคื—ื•ืช ืž-10ms ืขื‘ื•ืจ ืขื•ืžืกื™ ืขื‘ื•ื“ื” ืคืฉื•ื˜ื™ื ื™ื•ืชืจ ืฉืœ ื”ืขื‘ืจื” ืื• ืกื™ื ื•ืŸ ื‘ืืžืฆืขื•ืช ืžืขื‘ื“ื™ ื–ืจื (ืกื˜ืจื™ืžื™ื ื’) ื‘ื–ื™ื›ืจื•ืŸ ื›ืžื• Apache Flink ืื• Kafka Streams. ื”ื’ื•ืจืžื™ื ื”ืขื™ืงืจื™ื™ื ื”ืชื•ืจืžื™ื ืœื—ื‘ื™ื•ืŸ ื”ื ื‘ื“ืจืš ื›ืœืœ ืงืคื™ืฆื•ืช ืจืฉืช (network hops), ืชืงื•ืจื” ืฉืœ ืกืจื™ืืœื™ื–ืฆื™ื” (serialization overhead) ื•ืื™ื’ื•ื“ ื›ืชื™ื‘ื” ืœื›ื™ื•ืจ (sink write batching), ืฉืื•ืชื ืื ื• ืžื›ื•ื•ื ื ื™ื ื‘ื”ืชืื ืœื”ืขื“ืคื•ืช ื”ืคืฉืจื” ืฉืœื›ื ื‘ื™ืŸ ื—ื‘ื™ื•ืŸ ืœืชืคื•ืงื” (latency-versus-throughput tradeoff). ื‘ืžื”ืœืš ืชื›ื ื•ืŸ ื”ืืจื›ื™ื˜ืงื˜ื•ืจื” ืฉืœื ื•, ืื ื• ืงื•ื‘ืขื™ื ื™ืขื“ื™ ืจืžืช ืฉื™ืจื•ืช (SLOs) ืžืคื•ืจืฉื™ื ืœื—ื‘ื™ื•ืŸ ืขื‘ื•ืจ ื›ืœ ืฉืœื‘ ื‘ืฆื™ื ื•ืจ ื”ื ืชื•ื ื™ื ื•ื‘ื•ื ื™ื ืœื•ื—ื•ืช ืžื—ื•ื•ื ื™ื ืœื ื™ื˜ื•ืจ ื”ืขื•ืงื‘ื™ื ืื—ืจ ื—ื‘ื™ื•ืŸ p50, p95 ื•-p99 ื‘ืกื‘ื™ื‘ืช ื”ืคืจื•ื“ืงืฉืŸ.

MicrocosmWorks ืžื™ื™ืฉืžืช ืจืฉืžื™ ืกื›ืžื” (schema registries) (ื‘ื“ืจืš ื›ืœืœ Confluent Schema Registry ืื• AWS Glue Schema Registry) ื”ืื•ื›ืคื™ื ื›ืœืœื™ ืชืื™ืžื•ืช ืœืื—ื•ืจ ื•ืงื“ื™ืžื” (backward and forward compatibility), ื•ืžื‘ื˜ื™ื—ื™ื ืฉืžืคื™ืงื™ื ื™ื›ื•ืœื™ื ืœืคืชื— ืืช ืคื•ืจืžื˜ื™ ื”ื ืชื•ื ื™ื ืฉืœื”ื ืžื‘ืœื™ ืœืฉื‘ื•ืจ ืฆืจื›ื ื™ื ืงื™ื™ืžื™ื. ืื ื• ืžืฉืชืžืฉื™ื ื‘ืกืจื™ืืœื™ื–ืฆื™ื™ืช Avro ืื• Protobuf ืขื ื ื™ื”ื•ืœ ื’ืจืกืื•ืช ืกื›ืžื” ืžืคื•ืจืฉ (explicit schema versioning), ื›ืš ืฉื›ืœ ื”ื•ื“ืขื” ื”ื™ื ืžืชืืจืช ืืช ืขืฆืžื” ื•ื ื™ืชื ืช ืœื‘ื™ื˜ื•ืœ ืกืจื™ืืœื™ื–ืฆื™ื” (deserialized) ื’ื ืื ื”ืกื›ืžื” ื”ืฉืชื ืชื” ืžืื– ืฉื”ื•ืคืงื”. ืฆื™ื ื•ืจื•ืช ื”-CI/CD ืฉืœื ื• ื›ื•ืœืœื™ื ื‘ื“ื™ืงื•ืช ืชืื™ืžื•ืช ืกื›ืžื” ืื•ื˜ื•ืžื˜ื™ื•ืช ืฉื—ื•ืกืžื•ืช ืคืจื™ืกื•ืช ืื ืฉื™ื ื•ื™ ืกื›ืžื” ืžื•ืฆืข ืขืœื•ืœ ืœืฉื‘ื•ืจ ืฆืจื›ื ื™ื ื‘ืžื•ืจื“ ื”ื–ืจื (downstream consumers).

MicrocosmWorks ืžืžืœื™ืฆื” ืขืœ ืžื™ื ื™ืžื•ื 2-3 ืžื”ื ื“ืกื™ื ื‘ืขืœื™ ื ื™ืกื™ื•ืŸ ื‘ืžืขืจื›ื•ืช ืžื‘ื•ื–ืจื•ืช, ืžืกื’ืจื•ืช ืขื™ื‘ื•ื“ ื–ืจื (ืกื˜ืจื™ืžื™ื ื’) ื•ืื•ื˜ื•ืžืฆื™ื™ืช ืชืฉืชื™ืช, ื›ื“ื™ ืœืชื—ื–ืง ืคืœื˜ืคื•ืจืžืช ืกื˜ืจื™ืžื™ื ื’ ื‘ืคืจื•ื“ืงืฉืŸ ื‘ืื•ืคืŸ ืืžื™ืŸ. ืขื‘ื•ืจ ื—ื‘ืจื•ืช ืฉืื™ื ืŸ ืจื•ืฆื•ืช ืœื‘ื ื•ืช ืžื•ืžื—ื™ื•ืช ื–ื• ื‘ืื•ืคืŸ ืคื ื™ืžื™, ืื ื• ืžืฆื™ืขื™ื ืชืžื™ื›ื” ืžื ื•ื”ืœืช ื‘ืคืœื˜ืคื•ืจืžืช ืกื˜ืจื™ืžื™ื ื’ ื‘ืขืœื•ืช ืฉืœ 15$-40$/ืฉืขื”, ืฉื‘ื” ื”ืฆื•ื•ืช ืฉืœื ื• ืžื˜ืคืœ ื‘ืคืขื•ืœื•ืช ืืฉื›ื•ืœ (cluster operations), ื›ื•ื•ื ื•ืŸ ื‘ื™ืฆื•ืขื™ื (performance tuning) ื•ืชื’ื•ื‘ื” ืœืื™ืจื•ืขื™ื (incident response), ื‘ืขื•ื“ ืฉื”ืžืคืชื—ื™ื ืฉืœื›ื ืžืชืžืงื“ื™ื ื‘ื‘ื ื™ื™ืช ื™ื™ืฉื•ืžื™ ืขื™ื‘ื•ื“ ื–ืจื (ืกื˜ืจื™ืžื™ื ื’). ืื ื• ืžืกืคืงื™ื ื’ื ืชื•ื›ื ื™ื•ืช ื”ื“ืจื›ื” ื”ืžืฉื“ืจื’ื•ืช ืืช ื›ื™ืฉื•ืจื™ ืฆื•ื•ืช ื”ื”ื ื“ืกื” ื”ืงื™ื™ื ืฉืœื›ื ืขืœ ืคืขื•ืœื•ืช Kafka, Flink ืื• Kinesis, ื‘ืžืกื’ืจืช ื”ืชืงืฉืจื•ื™ื•ืช ืฉืœ 4-8 ืฉื‘ื•ืขื•ืช.