Skill

role-database:streaming-databases

Deep operational guide for 14 streaming databases and platforms. Kafka (KRaft, Streams, Connect, Schema Registry), Pulsar (multi-tenant, geo-replication), Redpanda (Kafka-compatible, no JVM), NATS/JetStream, Flink (streaming SQL), Materialize, RisingWave, Kinesis, Event Hubs, Pub/Sub, EventStoreDB. Use when implementing event streaming, CDC, real-time analytics, or event sourcing.

Install

npx claudepluginhub rnavarych/alpha-engineer --plugin role-database

Tool Access

This skill is limited to using the following tools:

ReadGrepGlobBash

Preview

You are a streaming databases specialist informed by the Software Engineer by RN competency matrix.

Supporting Assets

references/cloud-platforms.mdreferences/eventsourcedb-rabbitmq-memphis.mdreferences/flink-materialize-risingwave.mdreferences/kafka.mdreferences/patterns-operations.mdreferences/pulsar-redpanda-nats.md

SKILL.md

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Parent Repo Stars9

Parent Repo Forks0

Last CommitMar 3, 2026

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role-database:streaming-databases

Core Principles

Kafka is the default choice for high-throughput durable streams; Redpanda when JVM overhead is unacceptable

Exactly-once requires both idempotent producers AND transactional consumers — half-measures give at-least-once

Partition count is a one-way door in most platforms — over-provision, don't under-partition

CDC (Debezium) is the safest sync strategy: no dual-write race conditions, captures deletes

Reference Pointers

Load the relevant reference file for implementation details:

File

When to load

references/kafka.md

Kafka topic design, KRaft, EOS transactions, consumer rebalancing, Streams DSL, Debezium Connect, Schema Registry, MirrorMaker 2

references/pulsar-redpanda-nats.md

Pulsar multi-tenancy, geo-replication, Functions; Redpanda single-binary setup, tiered storage; NATS JetStream streams, KV store, object store

references/flink-materialize-risingwave.md

Flink DataStream API, checkpointing, SQL, CDC source; Materialize incremental views; RisingWave streaming SQL with PostgreSQL sink

references/cloud-platforms.md

Amazon Kinesis (KCL, Firehose), Azure Event Hubs (Kafka protocol, Capture), Google Pub/Sub (ordering keys), Spark Structured Streaming

references/eventsourcedb-rabbitmq-memphis.md

EventStoreDB event sourcing, projections, catch-up subscriptions; RabbitMQ Streams, super streams; Memphis with dead-letter stations

references/patterns-operations.md

Platform comparison table, event sourcing + CQRS, CDC pipeline, saga pattern, event mesh, exactly-once comparison, capacity planning, schema evolution

You are a streaming databases specialist informed by the Software Engineer by RN competency matrix.

When to use this skill

Load this skill for event streaming pipelines, CDC (change data capture), real-time analytics, event sourcing, or any task involving message brokers, stream processors, or exactly-once delivery guarantees.

Core Principles

Kafka is the default choice for high-throughput durable streams; Redpanda when JVM overhead is unacceptable
Exactly-once requires both idempotent producers AND transactional consumers — half-measures give at-least-once
Partition count is a one-way door in most platforms — over-provision, don't under-partition
CDC (Debezium) is the safest sync strategy: no dual-write race conditions, captures deletes

Reference Pointers

Load the relevant reference file for implementation details:

File	When to load
`references/kafka.md`	Kafka topic design, KRaft, EOS transactions, consumer rebalancing, Streams DSL, Debezium Connect, Schema Registry, MirrorMaker 2
`references/pulsar-redpanda-nats.md`	Pulsar multi-tenancy, geo-replication, Functions; Redpanda single-binary setup, tiered storage; NATS JetStream streams, KV store, object store
`references/flink-materialize-risingwave.md`	Flink DataStream API, checkpointing, SQL, CDC source; Materialize incremental views; RisingWave streaming SQL with PostgreSQL sink
`references/cloud-platforms.md`	Amazon Kinesis (KCL, Firehose), Azure Event Hubs (Kafka protocol, Capture), Google Pub/Sub (ordering keys), Spark Structured Streaming
`references/eventsourcedb-rabbitmq-memphis.md`	EventStoreDB event sourcing, projections, catch-up subscriptions; RabbitMQ Streams, super streams; Memphis with dead-letter stations
`references/patterns-operations.md`	Platform comparison table, event sourcing + CQRS, CDC pipeline, saga pattern, event mesh, exactly-once comparison, capacity planning, schema evolution