AutoMQ alternative — AutoMQ vs Skippr

AutoMQ is Apache-2.0 Kafka-compatible streaming with diskless brokers: the log lives in S3-compatible object storage, brokers stay stateless. Latest stable GitHub release on the 1.7 line is 1.7.4; 1.7.5 is still release-candidate (rc3 on 2 Nov 2026). Table Topic (AutoMQ 1.5+) embeds Iceberg materialization in the cluster: Kafka topics become Iceberg tables in S3, with schema evolution and catalogs AWS S3 Tables, Glue, or Hive. You must turn Table Topic on at cluster deploy; you cannot enable it later. Freshness follows the commit interval (shorter commits, more small files). FAQ says Iceberg is the table format today. Marketing also mentions Delta; treat Iceberg as the documented path. CDC is still a producer or connector writing the topic. Self-host is Apache-2.0; you pay object storage, VMs, and people. AutoMQ Cloud BYOC bills a subscription (usage: compressed ingress, egress, retention, cluster uptime on AWS as of August 2026; or prepaid AKU) plus cloud-resource fees to the provider. Cloud trial credit is 1,400 USD for 30 days. 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 AKU. ELT into a warehouse is one path, not the product.

Feature	Skippr	AutoMQ
Deploy	Single Rust binary, your VPC or on-prem	Diskless Kafka brokers on object storage, or AutoMQ Cloud BYOC
Schema	In-stream evolution, sibling columns	Kafka schemas. Table Topic propagates topic schema into Iceberg
Storage	Open Iceberg on Parquet you own	S3 Kafka log. Iceberg via Table Topic (on at deploy)
CDC	Log-based; fails closed if the dest cannot honour	A source writes the topic. Table Topic materializes it
Query	`skipprd query` on Iceberg ∪ WAL; history in any Iceberg engine	Kafka consumers. Iceberg after Table Topic (Athena, Spark, Trino, …)
Pricing	Free self-hosted engine	Apache-2.0 self-host. Cloud: AKU or usage plus cloud VMs/S3.

What AutoMQ is good at

Kafka protocol on object storage without broker disks, plus Table Topic when that same cluster must land Iceberg without Connect. That is the product. S3 Tables plus Athena is why some AWS estates pick it over a second Flink job. If the job is "this Kafka-compatible log, and Iceberg from the brokers," that is a real product.

Where the job is wider

AutoMQ is a Kafka runtime with an Iceberg materializer. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. Postgres is not a Table Topic source. CDC still starts before the topic. Table Topic is a deploy-time flag. Miss it and you rebuild the cluster.

Object-store math is the ops. Commit interval versus small files, catalog choice, and Cloud AKU or usage lines. Skippr will not replace an AutoMQ topic that already fans out. Skippr also will not run diskless brokers. WarpStream Tableflow and Confluent Tableflow are separate pages.

If the job is "AutoMQ Kafka we already staff," AutoMQ is the narrower tool. If the job is live SQL on Iceberg you run, Table Topic 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.

YAMLCLIPython

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 AutoMQ if you want Kafka protocol on object storage, and Table Topic as Iceberg from those brokers is enough.

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

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

AutoMQ alternative — AutoMQ vs Skippr ​

What AutoMQ is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

AutoMQ alternative — AutoMQ vs Skippr

What AutoMQ is good at

Where the job is wider

Getting started

Which to choose