Apache Kafka alternative — Apache Kafka vs Skippr

Apache Kafka is Apache-2.0 pub/sub. Brokers serve topics. Current release is 4.4.0 (22 Oct 2026). KRaft is the metadata plane. Producers, consumers, Connect, Streams, and (on Confluent Cloud) ksqlDB and Tableflow sit on the bus. Iceberg is not in the broker. Self-hosted Iceberg is the Kafka Connect sink (Iceberg 1.12.0, default commit 5 minutes) or a Flink/Spark writer. Confluent Tableflow is Cloud-only, not Confluent Platform. CDC is a source connector into a topic, not Kafka itself. There is no licence fee. You pay brokers, disks or tiered storage, ZooKeeper-no-more KRaft, and people. Cloud Kafka is eCKU/CKU plus ingress, egress, and storage (Confluent Standard lists about 0.75 USD an eCKU-hour and 0.08 USD a GB-month on their public grid; Basic first eCKU is free then 0.14 USD). 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 broker cluster. ELT into a warehouse is one path, not the product.

Feature	Skippr	Apache Kafka
Deploy	Single Rust binary, your VPC or on-prem	Brokers (KRaft), or a cloud Kafka
Schema	In-stream evolution, sibling columns	Topic bytes plus Schema Registry if you add it
Storage	Open Iceberg on Parquet you own	The log. Iceberg is Connect, Flink, or Tableflow after
CDC	Log-based; fails closed if the dest cannot honour	A source connector writes the topic. Kafka is the bus
Query	`skipprd query` on Iceberg ∪ WAL; history in any Iceberg engine	Consumers on the topic. Lake after a sink
Pricing	Free self-hosted engine	Apache-2.0. Cloud is eCKU plus GB.

What Apache Kafka is good at

A durable log many independent consumers share, with partitions as the scale unit, exactly-once produce when you opt in, and a connector ecosystem. That is the product. If the job is "this event onto this bus," that is a real product. Connect Iceberg or Tableflow is the landing path when the lake is a later reader.

Where the job is wider

Kafka is a message bus. It is not a Streamhouse. Apache Kafka does not query Iceberg ∪ WAL. Live SQL is not kafka-console-consumer. Iceberg is a sink you add. CDC still needs Debezium, DMS, or an app writing the topic. Confluent Tableflow will not run on self-hosted Kafka.

The cluster is the ops. Brokers, partitions, consumer groups, disk or object-store tiering, and a Connect or Flink fleet if you want tables. Skippr will not replace a Kafka topic that already fans out to many consumers. Skippr also will not run KRaft. Tableflow, Connect Iceberg, Streams, and ksqlDB are separate pages.

If the job is "Kafka we already staff," Kafka is the narrower tool. If the job is live SQL on Iceberg you run, the log 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 Kafka if you want a shared log, and the topic after produce is enough. Add Connect or Tableflow if that bus must also land Iceberg.

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

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

Apache Kafka alternative — Apache Kafka vs Skippr ​

What Apache Kafka is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

Apache Kafka alternative — Apache Kafka vs Skippr

What Apache Kafka is good at

Where the job is wider

Getting started

Which to choose