Artie alternative — Artie vs Skippr

Artie is SaaS log CDC. A control plane in Artie's cloud. The data plane is Artie-hosted, or Enterprise BYOC (you supply Kubernetes, Kafka, and storage). Postgres, MySQL, MongoDB, DynamoDB, and peers on Free and Growth. Oracle and SQL Server archive-log CDC are Enterprise. Destinations on Free/Growth are warehouses plus S3 and GCS. Iceberg (REST catalogs and S3 Tables) and Delta Lake are on the Enterprise dest list. Delivery is exactly-once. Sub-minute latency is the published target. Free is 1 million billable rows a month and two pipelines. Backfills never count. Growth starts at 500 USD a month billed annually (600 month-to-month), then usage on rows after the first snapshot. PrivateLink is 500 USD a month per connection on Growth. Enterprise is quote: Iceberg, Oracle, SQL Server, BYOC, 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 row meter. ELT into a warehouse is one path, not the product.

Feature	Skippr	Artie
Deploy	Single Rust binary, your VPC or on-prem	Artie Cloud, or Enterprise BYOC
Schema	In-stream evolution, sibling columns	Connector evolution into the dest
Storage	Open Iceberg on Parquet you own	Warehouse or object store. Iceberg and Delta are Enterprise
CDC	Log-based; fails closed if the dest cannot honour	Log CDC. Exactly-once. Backfill is free
Query	`skipprd query` on Iceberg ∪ WAL; history in any Iceberg engine	After the dest writer
Pricing	Free self-hosted engine	Free to 1M rows. Growth from 500 USD/mo. Iceberg is Enterprise.

What Artie is good at

A Postgres or MySQL log that needs to land in Snowflake without paying for the initial snapshot. That is the product. Two pipelines and a million rows free is why some teams start here before MAR. If the job is "this CDC into this warehouse, SaaS plane," that is a real product.

Where the job is wider

Artie is a CDC pipe. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. Iceberg REST and S3 Tables sit on Enterprise, not Growth.

Row math is the ops. High-churn tables bill every change after backfill. PrivateLink is a second 500 USD line. Skippr will not match DynamoDB or MongoDB the way Artie does. Skippr also will not replace an Artie pipeline that already lands Snowflake.

If the job is "Artie CDC we already staff," Artie is the narrower tool. If the job is live SQL on Iceberg you run, Artie 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 Artie if you want SaaS CDC billed on rows after a free backfill, and the warehouse after apply is enough.

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

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

Artie alternative — Artie vs Skippr ​

What Artie is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

Artie alternative — Artie vs Skippr

What Artie is good at

Where the job is wider

Getting started

Which to choose