Kafka Streams alternative — Kafka Streams vs Skippr

Kafka Streams is Apache Kafka's JVM library for stream processing inside your application. Current Kafka is 4.4.0 (22 Oct 2026). You add kafka-streams, write a topology (DSL or Processor API), and your service becomes the consumer group. State lives in local stores backed by changelog topics. Interactive queries (IQv2) read those stores. 4.4.0 adds transactional state stores (enable.transactional.statestores, default off, KIP-892), broker-side Streams group assignors (KIP-1357), topology description on the group (KIP-1331), and headers-aware IQv2 queries (KIP-1356). I/O is Kafka topics. There is no Iceberg sink in Streams. The Iceberg path is still the Kafka Connect Iceberg connector (Iceberg 1.12.0) or Tableflow on Confluent Cloud. CDC starts before Streams: Debezium or a producer writes the topic. There is no licence fee for Apache Kafka. You pay brokers, changelogs, disks, and the JVM. Confluent Cloud also bills a Kafka Streams line per eCKU/CKU-hour on clusters with a connected Streams app. 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 changelog topic. ELT into a warehouse is one path, not the product.

Feature	Skippr	Kafka Streams
Deploy	Single Rust binary, your VPC or on-prem	JAR in your app, plus a Kafka cluster
Schema	In-stream evolution, sibling columns	Serdes plus Schema Registry if you add it
Storage	Open Iceberg on Parquet you own	Changelog-backed state stores. Iceberg is Connect or Tableflow after
CDC	Log-based; fails closed if the dest cannot honour	Reads CDC already on a topic. Capture is Debezium or a producer
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Interactive queries on local stores. Lake is a later sink
Pricing	Free self-hosted engine	Apache-2.0. Pay Kafka. Confluent Cloud adds a Streams eCKU/CKU line.

Talk through skipprd now

Paul Bot knows the docs. Google Meet as soon as you join, any time of day.

Chat now →

Any time · 20 minutes · Google Meet now

What Kafka Streams is good at

Custom stream processing in the same JVM service that already speaks Kafka, without standing up a Flink or ksqlDB cluster. That is the product. Processor API, punctuators, and now transactional stores are why app teams still embed it. If the job is "this topology in our service," that is a real product.

Where the job is wider

Kafka Streams is a library over Kafka. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight Iceberg rows. Postgres is not a Streams source. Iceberg starts after: Connect or Tableflow. Restore time on a large changelog is still the restart tax unless you turn on transactional stores and accept the new buffer.

The cluster is the ops. Brokers, partitions as the scale cap, rebalances, state dirs. Skippr will not run a Streams topology. Skippr also will not replace a Kafka bus. ksqlDB is SQL on the same engine (separate page).

If the job is "Kafka Streams we already embed," Streams is the narrower tool. If the job is live SQL on Iceberg you run, the library 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

CLI

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")

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 Streams if you want a JVM topology inside a service that already speaks Kafka, and the next topic is the job.

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

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