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RabbitMQ alternative — RabbitMQ vs Skippr ​

RabbitMQ is an MPL-2.0 broker. Queues, exchanges, and bindings are the product. Latest open-source release is 4.4.0 (20 Oct 2026): quorum-queue delayed and deferred messages, native STOMP, AMQP 1.0 connection uniqueness, Erlang 29. Streams are a separate log-shaped queue type (x-stream-initial-offset from 4.4). Iceberg is not in RabbitMQ. There is no first-party lake sink. CDC is whatever publishes to an exchange. Self-host is MPL-2.0; you pay Erlang nodes and disk. Commercial: VMware Tanzu RabbitMQ (4.4 adds exchange federation v2 and JMS topics over AMQP 1.0) and Amazon MQ for RabbitMQ. skipprd can consume an AMQP 0-9-1 queue and ACK only after the WAL owns the message; that is a source, not a broker. This is not Iceberg ∪ WAL you query in one binary.

Skippr is an open Streamhouse you run as one Rust binary. Write skippr.yml (or the same thing in Python), discover, sync. You query live state over Iceberg ∪ WAL, and the history is ordinary Iceberg in your bucket. No RabbitMQ cluster. ELT into a warehouse is one path, not the product.

FeatureSkipprRabbitMQ
DeploySingle Rust binary, your VPC or on-premErlang nodes, or Tanzu / Amazon MQ
SchemaIn-stream evolution, sibling columnsMessage bytes plus optional headers
StorageOpen Iceberg on Parquet you ownQueue / quorum / stream on disk. No Iceberg
CDCLog-based; fails closed if the dest cannot honourA publisher. RabbitMQ is the broker
Queryskipprd query on Iceberg ∪ WAL; history in any Iceberg engineConsumers. Lake after an application sink
PricingFree self-hosted engineMPL-2.0. Tanzu and Amazon MQ are paid.

What RabbitMQ is good at ​

Work queues, routing keys, and (now) quorum-queue delayed retries, with streams when you want offset replay instead of competing consumers. That is the product. AMQP 0-9-1 plus 1.0, MQTT, STOMP, and a management UI is why a lot of application estates never moved to Kafka. If the job is "this message onto this queue," that is a real product. skipprd AMQP is the landing path when you already have that queue and want Iceberg afterwards.

Where the job is wider ​

RabbitMQ is a broker. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight lake rows. Streams are still RabbitMQ streams. Postgres CDC still starts in the database, not in an exchange. skipprd AMQP consumes one queue; it will not replace the cluster, and it is not fail-closed lake CDC from WAL.

Nodes, mirrored or quorum members, and stream retention are the ops. Skippr will not replace a RabbitMQ vhost that already fans out to many workers. Skippr also will not run rabbitmq-server. Kafka is a separate page.

If the job is "RabbitMQ we already staff," RabbitMQ is the narrower tool. If the job is live SQL on Iceberg you run, the queue was never that.

Getting started ​

Install skipprd from the install guide, or pip install skippr. The three tabs write the same skippr.yml. Then discover, sync, and query.

yaml
skippr:
  workspace: analytics
  skipprd_el_storage_mode: local

pipelines:
  orders:
    data_source: data_sources.app_db
    data_sink: data_sinks.lake

data_sources:
  app_db:
    Postgres:
      host: db.internal
      user: skippr_reader
      password: ${APP_DB_PASSWORD}
      database: app
      tables: [orders, customers]
      cdc_mode: snapshot_then_cdc

data_sinks:
  lake:
    AthenaIceberg:
      warehouse: s3://my-iceberg-warehouse/
      glue_database_name: analytics
      athena_workgroup_name: primary
      athena_results_s3_bucket: my-athena-results
      region: us-east-1
    schema_sink: schema_sinks.lake_schema

schema_sinks:
  lake_schema:
    AthenaIceberg:
      warehouse: s3://my-iceberg-warehouse/
      glue_database_name: analytics
      athena_workgroup_name: primary
      athena_results_s3_bucket: my-athena-results
      region: us-east-1
bash
skipprd --workspace analytics --storage-mode local connect data-source postgres \
  --pipeline orders \
  --name app_db \
  --host db.internal \
  --user skippr_reader \
  --password '${APP_DB_PASSWORD}' \
  --database app \
  --tables orders \
  --tables customers \
  --cdc-mode snapshot_then_cdc

skipprd connect data-sink athenaiceberg \
  --pipeline orders \
  --name lake \
  --warehouse s3://my-iceberg-warehouse/ \
  --glue-database-name analytics \
  --athena-workgroup-name primary \
  --athena-results-s3-bucket my-athena-results \
  --region us-east-1

skipprd connect schema-sink athenaiceberg \
  --pipeline orders \
  --name lake_schema
python
from skippr import (
    Config,
    DataSinkAthenaIceberg,
    DataSourcePostgres,
    DataSourcePostgresSourceCdcMode,
    EnvRef,
    LocalStorage,
    Pipeline,
)

cfg = Config().workspace("analytics").storage(LocalStorage())

app_db = cfg.data_source(
    "app_db",
    DataSourcePostgres(
        host="db.internal",
        user="skippr_reader",
        password=EnvRef("APP_DB_PASSWORD"),
        database="app",
        tables=["orders", "customers"],
        cdc_mode=DataSourcePostgresSourceCdcMode.SNAPSHOTTHENCDC,
    ),
)

lake = cfg.data_sink(
    "lake",
    DataSinkAthenaIceberg(
        warehouse="s3://my-iceberg-warehouse/",
        glue_database_name="analytics",
        athena_workgroup_name="primary",
        athena_results_s3_bucket="my-athena-results",
        region="us-east-1",
    ),
    schema_sink="lake_schema",
)

cfg.pipeline("orders", Pipeline(data_source=app_db, data_sink=lake))
cfg.save("skippr.yml")
bash
export APP_DB_PASSWORD='your-postgres-password'
skipprd discover --pipeline orders
skipprd sync --pipeline orders --once
skipprd query "SELECT count(*) FROM analytics.orders"

Which to choose ​

Choose RabbitMQ if you want a work-queue broker, and consuming that queue later (including with skipprd AMQP) is enough.

Choose Skippr if you want one binary, live SQL on Iceberg ∪ WAL, and files that stay yours. Skippr will not run RabbitMQ.

Install from Install. Connector and query reference live in Developer Docs. For optional onboarding in your VPC, Enterprise Jumpstart or Contact.