'kafka' 검색 결과

Run and operate the DeepStream Multi-View 3D Tracking reference app, also known as MV3DT. Use when the user asks to set up prerequisites, run shipped MV3DT samples, run Multi-View 3D Tracking on custom synchronized MP4 datasets, import camera calibration, delegate missing calibration to AutoMagicCalib, inspect OSD or BEV visualization, consume MV3DT Kafka metadata, or clean up MV3DT run state in the DeepStream MV3DT app directory.

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World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality monitoring, and DataOps. Use when designing data architectures, building batch or streaming data pipelines, optimizing data workflows, or implementing data governance.

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Use when you need to select, review, or implement Java design and integration patterns — including classic Java design patterns, REST API patterns, Kafka and event-driven patterns, database and persistence patterns, and cross-cutting integration patterns. This should trigger for requests such as Apply Java design patterns; Review REST API patterns; Design Kafka event-driven patterns; Improve database persistence patterns; Add resilient integration patterns. Part of Plinth Toolkit

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Provides Event-Driven Architecture (EDA) patterns for Spring Boot — creates domain events, configures ApplicationEvent and @TransactionalEventListener, sets up Kafka producers and consumers, and implements the transactional outbox pattern for reliable distributed messaging. Use when implementing event-driven systems in Spring Boot, setting up async messaging with Kafka, publishing domain events from DDD aggregates, or needing reliable event publishing with the outbox pattern.

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Event sourcing and CQRS expert for AI memory systemsUse when "event sourcing, event store, cqrs, nats jetstream, kafka events, event projection, replay events, event schema, event-sourcing, cqrs, nats, kafka, projections, event-driven, memory-architecture, ml-memory" mentioned.

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Processes real-time event streams using KafkaJS consumer groups and transforms messages with configurable schemas. Handles partition rebalancing, offset commits, and dead-letter queue routing for failed transformations.

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Use when building data pipelines, ETL jobs, event processors, message-broker producers/consumers, application caching layers, database migrations, BigQuery queries, or event-sourcing handlers in TypeScript. Triggers on edits to indexer/ETL/migration code, Prisma or Drizzle schemas, BigQuery integration code, RabbitMQ/Kafka/SQS/BullMQ producer or consumer code, Redis cache wrappers, or mentions of "data engineering", "ETL", "pipeline", "indexer", "event sourcing", "data warehouse", "data migration", "message queue", "RabbitMQ", "Kafka", "SQS", "BullMQ", "Redis cache", or "caching".

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Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).

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Apache Kafka 4.2 best practices for event streaming, topic design, and producer-consumer patterns

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Best practices and guidelines for Apache Kafka event streaming and distributed messaging

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Provides Event-Driven Architecture (EDA) patterns for Spring Boot — creates domain events, configures ApplicationEvent and @TransactionalEventListener, sets up Kafka producers and consumers, and implements the transactional outbox pattern for reliable distributed messaging. Use when implementing event-driven systems in Spring Boot, setting up async messaging with Kafka, publishing domain events from DDD aggregates, or needing reliable event publishing with the outbox pattern.

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Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).

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Expert knowledge of A1 statistics publishing to data warehouse in melosys-api. Use when: (1) Understanding utstedt A1 Kafka publishing, (2) Debugging UtstedtA1Service or Kafka producer issues, (3) Understanding DVH/data warehouse integration, (4) Investigating A1 statistics message format and schema.

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External integrations overview for melosys-api including REST/SOAP services, Kafka topics, databases, and support services. Use when adding new integrations, debugging external calls, understanding data flow, or troubleshooting connectivity. Triggers: "integration", "external service", "consumer", "kafka topic", "REST client".

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Flox로 개발 도구와 데이터베이스의 구성 및 버전을 설정 파일에 정의합니다. macOS와 Linux에서 재현할 수 있는 개발 환경을 만들고 팀과 공유합니다.

• 팀의 개발 환경 통일
• 언어 도구와 시스템 의존성 관리
• PostgreSQL과 Redis 로컬 실행

Coordinate groups of AI agents across multiple GitHub repositories. Analyze dependencies, organize shared updates and refactoring, run tests, and create linked pull requests so related projects can be changed together.

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• Updating dependencies across multiple repositories
• Coordinating shared API and library changes
• Applying security patches across projects

여러 GitHub 저장소의 변경을 조율하고 의존 패키지 버전과 문서를 맞춥니다. 여러 AI 에이전트가 통합 테스트와 구성 표준화도 나누어 수행합니다.

• 여러 저장소의 의존 버전 통일
• 공유 라이브러리를 쓰는 프로젝트 업데이트
• 문서와 CI 구성 표준화

Design storage for event sourcing, an approach that records changes as events. Compare database options and plan event ordering, duplicate handling, concurrent writes, and scaling to build a consistent event persistence layer.

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• Compare event storage technologies
• Design event schemas and streams
• Handle concurrent and duplicate writes

Build Python applications that collect live cryptocurrency prices, trades, and order books with Cryptofeed. Covers consistent data formats across exchanges, price comparisons, and writing incoming data to storage systems for monitoring and analysis.

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• Monitoring prices across cryptocurrency exchanges
• Analyzing order book depth
• Analyzing executed trade flows

Deploy and manage applications on Cloud Run using source code or container images. Choose services for web requests, jobs for tasks that finish, or worker pools for continuous background processing, with guidance on execution settings and deployment failures.

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• Deploy a web application from source code or a container
• Create and run batch jobs with parallel tasks
• Deploy continuous queue-processing workers

Guides Java SDK development in Apache Beam, from finding relevant modules to building code, running focused tests, and trying examples. Also explains how to publish modified modules locally and build SDK containers.

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• Finding the Java SDK module to modify
• Running a specific test class or method
• Checking setup with the WordCount example

Create ADRs, short records of architectural decisions, that explain what was chosen, why, and with what trade-offs. Choose among three formats and organize dates, status, numbering, and related links for future team members.

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• Recording why a database was chosen
• Preserving the rationale for a framework choice
• Documenting an authentication approach

Find the relevant architectural guidance for API design, security, cloud infrastructure, message queues, and caching. This skill acts as an index to specialized references and lists principles for structuring software, distributed systems, and access controls.

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• Finding guidance for API design
• Reviewing security architecture topics
• Exploring containers, K8s, and Serverless

Use this skill when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection (GEBD) plus VLM classification. Trigger even if the user does not name it: verify operator step sequence, detect missing or out-of-order SOP steps, score factory/work-cell video for procedure compliance, run VLM-based SOP checking on industrial cameras, or call /v1/chat/completions with a file, RTSP, or Basler camera. Also trigger for its internals: SOPVideoProcessor, DeepStream GEBD model (e.g. DDM) via Triton CAPI, nvds_custom_postprocess, Cosmos Reason 1/2 vLLM, SSE streaming, Kafka NvProto/JSON output, Basler/Pylon camera + emulation, Docker compose, chunk-level latency. Do NOT trigger for generic DeepStream pipelines, object detection/tracking, NIM imports, or video summarization.

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