claude

data-streaming-engineering

Data-streaming-engineering team — agents (streaming-architect, kafka-pipeline-engineer, stream-processing-engineer) for REAL-TIME data: streaming-vs-batch decision and topology, event streaming + CDC (Kafka/Pulsar/Kinesis, topics/partitioning, schema registry + evolution, the outbox/Debezium CDC pattern), delivery semantics (at-least-once vs exactly-once, idempotency, transactional outbox), and stream processing (Flink/Kafka-Streams: event-time vs processing-time, windowing, watermarks, state, joins, backpressure). skills, a decision-tree knowledge bank (streaming-vs-batch + delivery-semantics trees + a dated 2026 map), best-practices, templates, commands, an advisory hook. Distinct from data-platform (batch ELT) and microsoft-fabric (RTI/Eventstream). Seams: batch ELT -> data-platform, AsyncAPI contracts -> api-engineering, Fabric RTI -> microsoft-fabric, app-side messaging -> backend-engineering. Requires ravenclaude-core@>=0.7.0.

9 on GitHubby Matt Corbettv0.3.5MITupdated today
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Plugin data-streaming-engineering from marketplace ravenclaude in mcorbett51090/RavenClaude. Commands read from the repo's manifest — today.

This plugin runs code on your machine — its hooks fire automatically on events like tool use. The exact commands are listed below; read them before installing.

What it installs

Skills (7)

  • cdc-pipeline-setup

    Step-by-step playbook for standing up a Change Data Capture pipeline with Debezium — source connector configuration, slot management, snapshot strategy, and handling common failure modes.

  • consumer-lag-triage

    Structured triage procedure for diagnosing and resolving Kafka consumer group lag — distinguishes slow consumer, under-partitioned topic, rebalance storm, and broker-side causes.

  • kafka-platform

    Run the streaming platform reliably: partition for the ordering you need (order is per-partition; the key is the guarantee), govern schemas with a registry + compatibility rules, configure producer durability (acks/idempotence) and consumer offsets/idempotency, and ingest via CDC not dual-writes.

  • schema-evolution-playbook

    Step-by-step playbook for evolving Avro/Protobuf/JSON Schema event schemas without breaking consumers, covering compatibility modes, migration patterns, and registry operations.

  • schema-registry-evolution

    Playbook for managing Avro/Protobuf/JSON Schema schemas in a registry — choosing a compatibility mode, executing safe schema changes, and handling breaking changes without consumer downtime.

  • stream-processing

    Process streams correctly: aggregate on event-time with watermarks (not processing-time), window deliberately (tumbling/sliding/session), handle late data explicitly, checkpoint and TTL-bound state, join with aligned time, and design for backpressure.

  • streaming-vs-batch

    Decide streaming vs batch honestly by the real latency need (sub-minute reaction -> streaming; hourly/daily -> batch via data-platform), then design the topology, platform choice, and delivery semantics if streaming is justified.

Slash commands (4)

  • /data-streaming-engineering:build-stream-processor

    Build a correct stream processing job: event-time windowing with watermarks, late-data handling, bounded state, backpressure.

  • /data-streaming-engineering:decide-streaming-vs-batch

    Decide honestly whether a need requires streaming or batch; if streaming, sketch the topology and delivery semantics.

  • /data-streaming-engineering:design-topics

    Design topics and partitioning keyed for required ordering, with schema-registry governance and retention by purpose.

  • /data-streaming-engineering:setup-cdc

    Set up CDC from a database into the stream via Debezium + the transactional outbox, avoiding dual-writes.

Subagents (3)

  • kafka-pipeline-engineer

    Use for the streaming platform and ingestion: topic/partition design keyed for ordering, schema registry + compatibility/evolution, producer durability and consumer offset/idempotency discipline, and CDC via Debezium + transactional outbox. Routes processing to stream-processing-engineer.

  • stream-processing-engineer

    Use for stream processing (Flink/Kafka Streams): event-time vs processing-time, windowing (tumbling/sliding/session) with watermarks, explicit late-data handling, checkpointed and TTL-bounded state, stream-stream and stream-table joins, and backpressure/consumer-lag management.

  • streaming-architect

    Use for streaming architecture: the streaming-vs-batch decision by real latency need, end-to-end topology design (sources/topics/processors/sinks) with partitioning, platform selection (Kafka/Pulsar/Kinesis), delivery-semantics strategy, and CDC via Debezium/outbox rather than dual-writes.

Hooks (1)

  • PreToolUse

    /usr/bin/env -u BASH_ENV -u ENV -u SHELLOPTS -u BASHOPTS -u LD_PRELOAD -u LD_LIBRARY_PATH -u LD_AUDIT -u DYLD_INSERT_LIBRARIES -u DYLD_LIBRARY_PATH -u PYTHONPATH -u PYTHONSTARTUP PATH=/usr/local/bin:/

Language servers (3)

  • java
  • python
  • typescript
View source on GitHub mcorbett51090.github.io

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