ksqlDB alternative — ksqlDB vs Skippr

ksqlDB is SQL stream processing on Apache Kafka. You declare streams and tables over topics, joins and aggregations in SQL, and persistent queries that write more topics. Confluent Cloud hosts it: Confluent Streaming Units billed per hour, smallest cluster 4 CSUs, largest 28, sizes in steps of 4. High availability starts at 8 CSUs. Each CSU includes 125 GB of storage; at 8 CSUs and up, replicas take half. Caps on Cloud include 40 persistent queries per cluster, 15 ksqlDB clusters per environment, and no user-defined functions. Cloud ksqlDB is 8.3.0-0 as of 17 Jun 2026. Confluent's docs now recommend Confluent Cloud for Apache Flink for new stream processing; ksqlDB stays supported for existing apps. On-prem ksqlDB ships in Confluent Platform as a commercial component. Iceberg is not a ksqlDB sink. The Confluent Iceberg path is Tableflow (0.10 USD a topic-hour plus 0.04 USD a GB on Confluent's May 2026 TCO post) or a Kafka Connect Iceberg connector after the query. CSU list price is shown in the Cloud Console at provision and varies by region. Kafka eCKUs, ingress, and storage are extra. 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 CSU. ELT into a warehouse is one path, not the product.

Feature	Skippr	ksqlDB
Deploy	Single Rust binary, your VPC or on-prem	Confluent Cloud ksqlDB, or Confluent Platform
Schema	In-stream evolution, sibling columns	Schema Registry plus SQL `CREATE STREAM` / `CREATE TABLE`
Storage	Open Iceberg on Parquet you own	Kafka topics. Iceberg is Tableflow or Connect 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	Push and pull queries on streams/tables. Lake is a later sink
Pricing	Free self-hosted engine	CSU/hour (min 4). Kafka bill extra. CSU rate shown at provision.

What ksqlDB is good at

SQL over Kafka topics you already run, with persistent queries that write more topics, without writing Kafka Streams Java. That is the product. Schema Registry in the same Cloud environment is why some teams keep it. If the job is "this topic into that topic in SQL," that is a real product.

Where the job is wider

ksqlDB is a Kafka SQL engine. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight Iceberg rows. Postgres is not a ksqlDB source. CDC starts before ksqlDB: Debezium or an app still writes the topic. Iceberg starts after: Tableflow or Connect.

CSU math is the ops on Cloud. You pick the size at provision; scaling later is a support ticket or a new cluster. Flink is Confluent's stated path for new work. Skippr will not run ksqlDB persistent queries. Skippr also will not replace a Kafka bus. Tableflow is a separate page. Debezium is another.

If the job is "ksqlDB on Kafka we already staff," ksqlDB is the narrower tool. If the job is live SQL on Iceberg you run, ksqlDB 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 ksqlDB if you want SQL over Kafka topics you already run, 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 ksqlDB cluster.

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

ksqlDB alternative — ksqlDB vs Skippr ​

What ksqlDB is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

ksqlDB alternative — ksqlDB vs Skippr

What ksqlDB is good at

Where the job is wider

Getting started

Which to choose