Maxwell alternative — Maxwell vs Skippr

Maxwell (Maxwell's Daemon) is Apache-2.0 MySQL binlog CDC. One Java process reads the binlog as a replica and writes JSON. Current release is 1.46.0 (23 Sep 2026). Producers are stdout, Kafka, Kinesis, RabbitMQ, Redis, and a few peers. Bootstrap copies a table once, then the binlog takes over. 1.45.0 decompresses MySQL 8.4 binlogs. Schema lives in a maxwell database (you can bootstrap from a replica that does not hold that schema). There is no cluster mode. Throughput is one parser. There is no Postgres source. There is no Iceberg sink. The lake starts after you consume the JSON. This is not a Streamhouse. There is no Iceberg ∪ WAL.

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 Maxwell schema DB. ELT into a warehouse is one path, not the product.

Feature	Skippr	Maxwell
Deploy	Single Rust binary, your VPC or on-prem	One JVM. Docker `osheroff/maxwell` on 1.46.0
Schema	In-stream evolution, sibling columns	JSON fields from binlog. DDL updates Maxwell's schema store
Storage	Open Iceberg on Parquet you own	The producer dest. Iceberg is a sink you add
CDC	Log-based; fails closed if the dest cannot honour	MySQL binlog. Bootstrap then stream. Not Postgres
Query	`skipprd query` on Iceberg ∪ WAL; history in any Iceberg engine	After Kafka/Kinesis. Maxwell is the parse, not the lake
Pricing	Free self-hosted engine	Apache-2.0. Pay the JVM and the dest.

What Maxwell is good at

A MySQL binlog you want as JSON on a topic tonight, without ZooKeeper or Connect. That is the product. Bootstrap plus one process is why small teams still run it. If the job is "this binlog as JSON on Kafka," that is a real product.

Where the job is wider

Maxwell is a MySQL parser. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. Iceberg is a Kafka Connect or Flink job after Maxwell. One thread is the cap.

The schema database is the ops. Lose it and bootstrap again. Skippr will not emit Maxwell JSON. Skippr also will not replace a Maxwell topic that already feeds many consumers. Canal is the clustered MySQL sibling. Debezium is the multi-database sibling.

If the job is "Maxwell MySQL CDC we already staff," Maxwell is the narrower tool. If the job is live SQL on Iceberg you run, Maxwell 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 Maxwell if you want MySQL binlog as JSON on Kafka or Kinesis, and the topic after parse is enough.

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

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

Maxwell alternative — Maxwell vs Skippr ​

What Maxwell is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

Maxwell alternative — Maxwell vs Skippr

What Maxwell is good at

Where the job is wider

Getting started

Which to choose