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Snowflake alternative — Snowflake vs Skippr ​

Snowflake is a cloud warehouse. You load data, spin a virtual warehouse, and run SQL. Iceberg tables are first-class: Snowflake bills warehouse compute and cloud services when you work them, plus automated refresh or an external engine through Horizon Catalog if you use those. The Service Consumption Table is effective 29 Oct 2026. Warehouses consume Platform Credits with a one-minute minimum on start or resume, then per second. Cloud Services is 4.4 credits per hour, waived that day when it stays at or under 10 percent of warehouse credits. Serverless features bill one Platform Credit per Compute-Hour (an XS warehouse-hour equivalent) times a feature multiplier. Postgres CDC inside Snowflake is data mirroring, billed as a serverless apply task. Ingest SKUs (Openflow, Snowpipe) are separate pages. 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 Platform Credit for a sync. ELT into a warehouse is one path, not the product.

FeatureSkipprSnowflake
DeploySingle Rust binary, your VPC or on-premSnowflake account. Warehouses you size
SchemaIn-stream evolution, sibling columnsTable DDL. Iceberg schema in the catalog Snowflake uses
StorageOpen Iceberg on Parquet you ownSnowflake storage, or Iceberg in your bucket
CDCLog-based; fails closed if the dest cannot honourMirroring, Openflow, Snowpipe, or a tool you wire
Queryskipprd query on Iceberg ∪ WAL; history in any Iceberg engineSnowflake SQL after the table exists
PricingFree self-hosted enginePlatform Credits. See their consumption table.

What Snowflake is good at ​

Warehouse SQL the rest of the company already speaks, on tables Snowflake governs, including Iceberg it can read and write. That is the product. Credits, roles, and sharing are why some lakes still land in Snowflake even when the files sit in a bucket. If the job is "this warehouse, and Iceberg as a Snowflake table," that is a real product.

Where the job is wider ​

Snowflake is a warehouse. It is not a Streamhouse. There is no Iceberg ∪ WAL you query in one binary on your machine. There is no skipprd query on in-flight lake rows without a warehouse running. CDC still starts in mirroring, Openflow, Snowpipe, or skipprd. Skippr will not replace Snowflake warehouses, Horizon, or credit billing.

The warehouse (and Cloud Services) is the ops. Skippr will not run Snowflake SQL. Openflow and Snowpipe are separate pages.

If the job is "Snowflake we already staff," Snowflake is the narrower tool. If the job is live SQL on Iceberg you run, the warehouse 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 Snowflake if you want a warehouse, and Iceberg as a Snowflake table (or a native table) is enough.

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

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