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Streamkap alternative — Streamkap vs Skippr ​

Streamkap is managed log CDC on Kafka plus Flink. You point a source (Postgres, MySQL, DynamoDB, and peers) at a dest (warehouses, Iceberg). Streaming Agents transform in SQL, Python, or TypeScript. Iceberg dest talks Glue, Hive, or REST catalogs, and S3, GCS, or Azure Blob. Schema evolution is on. Billing is uncompressed GB, the higher of inbound or outbound, not both legs. Their planning figure is about 5 million rows per GB. Free is 500 MB a month; idle pipelines pause after a week. Starter is 250 USD a month for 1 GB, 1.50 USD a GB over that, a 30 GB monthly cap, 5 GB free backfill, 100 topics or tables. Scale is 2,100 USD a month (1,750 billed yearly) for 150 GB, same overage, 1 TB free backfill, 99.9 percent SLA, dedicated cloud and BYOC on the menu. Transform compute is 250 USD a vCPU-month. Enterprise is quote, BYOC in your VPC, 99.99 percent SLA. 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 GB floor. ELT into a warehouse is one path, not the product.

FeatureSkipprStreamkap
DeploySingle Rust binary, your VPC or on-premStreamkap Cloud, dedicated, or Enterprise BYOC
SchemaIn-stream evolution, sibling columnsConnector evolution. Agents for in-flight SQL/Python/TS
StorageOpen Iceberg on Parquet you ownDest warehouse, or Iceberg on Glue/Hive/REST
CDCLog-based; fails closed if the dest cannot honourLog CDC on managed Kafka. Snapshot plus changes
Queryskipprd query on Iceberg ∪ WAL; history in any Iceberg engineAfter the dest writer. Flink agents are not lake SQL
PricingFree self-hosted engineGB max(in, out). Starter 250 USD. Scale 2,100 USD.

What Streamkap is good at ​

A Postgres or MySQL log that needs sub-second apply into Snowflake or Iceberg without you running Connect. That is the product. Max(in, out) GB is simpler than MAR or two-leg capture billing. If the job is "managed Kafka CDC we do not want to staff," that is a real product.

Where the job is wider ​

Streamkap is a managed CDC pipe. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. Iceberg here is a dest connector. Kafka and Flink stay Streamkap's.

GB math is the ops. Starter's 1 GB included and 30 GB cap run out on a wide snapshot. Transform vCPUs are a second line. Skippr will not give you their agent runtime or DynamoDB CDC. Skippr also will not replace a Streamkap project that already writes Iceberg through Glue.

If the job is "Streamkap CDC we already staff," Streamkap is the narrower tool. If the job is live SQL on Iceberg you run, Streamkap 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
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 Streamkap if you want managed Kafka/Flink CDC billed on GB, and the dest after apply is enough.

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

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