dbt alternative — dbt vs Skippr
dbt is SQL transform on a warehouse (or, now, on Iceberg compute you opt into). dbt v2 is GA: the Rust Fusion engine shipped as dbt; the Apache-2.0 subset is dbt-oss. dbt Core is still Apache-2.0 as dbt v1. Latest listed patch is 2.0.5 (18 Sep 2026). Iceberg locally: DuckDB in dbt v2 with use_catalogs_v2 and a catalogs.yml entry; table and incremental materializations only, every Iceberg model needs a catalog_name (REST catalogs including Glue and S3 Tables). Lake Compute is private beta: hosted DuckDB against Iceberg, sidecar next to Snowflake/Databricks or as the default adapter with a Fivetran MDLS catalog. It is not GA. Beta is AWS us-east-1 / us-west-2, gated by DBT_ENGINE_EXPERIMENTAL_MULTI_ADAPTER=true, no JDBC/ODBC, no distributed joins. dbt does not ingest CDC. Platform pricing: Developer free (1 seat, 3,000 successful models/month, 1 project); Starter 100 USD per user-month (5 seats, 15,000 models, Semantic Layer); Enterprise and Enterprise+ are quotes. 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. Skippr will not compile ref(). ELT into a warehouse is one path, not the product.
| Feature | Skippr | dbt |
|---|---|---|
| Deploy | Single Rust binary, your VPC or on-prem | CLI (dbt / dbt-oss / v1), or dbt platform |
| Schema | In-stream evolution, sibling columns | Models, sources, tests. Catalogs in v2 catalogs.yml |
| Storage | Open Iceberg on Parquet you own | The warehouse, or Iceberg via DuckDB / Lake Compute (beta) |
| CDC | Log-based; fails closed if the dest cannot honour | Not dbt. A loader writes the source relation |
| Query | skipprd query on Iceberg ∪ WAL; history in any Iceberg engine | dbt show / warehouse SQL after the run |
| Pricing | Free self-hosted engine | CLI free. Platform from 100 USD/user-month (Starter). |
What dbt is good at
Versioned SQL models, tests, and lineage on the warehouse you already pay, with a DAG of refs that a whole team can review in Git. That is the product. DuckDB Iceberg in v2, and Lake Compute when it leaves beta, are why some projects want open tables without moving every model off Snowflake tomorrow. If the job is "this SELECT into this relation," that is a real product.
Where the job is wider
dbt is a transform compiler. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight lake rows. Postgres CDC still starts in Fivetran, Airbyte, or skipprd. Lake Compute will not replace a warehouse, and dbt says so. Skippr will not replace dbt Mesh, the Semantic Layer, or your existing models.
The warehouse bill (or the beta Lake Compute sidecar) is the ops. Skippr will not run dbt build. Fivetran is a separate page. Airflow and Dagster are separate pages if those schedule the project.
If the job is "dbt we already staff," dbt is the narrower tool. If the job is live SQL on Iceberg you run, the compiler 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.
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: ${APP_DB_PASSWORD}
database: app
tables: [orders, customers]
cdc_mode: snapshot_then_cdc
data_sinks:
lake:
AthenaIceberg:
warehouse: s3://my-iceberg-warehouse/
glue_database_name: analytics
athena_workgroup_name: primary
athena_results_s3_bucket: my-athena-results
region: us-east-1
schema_sink: schema_sinks.lake_schema
schema_sinks:
lake_schema:
AthenaIceberg:
warehouse: s3://my-iceberg-warehouse/
glue_database_name: analytics
athena_workgroup_name: primary
athena_results_s3_bucket: my-athena-results
region: us-east-1skipprd --workspace analytics --storage-mode local connect data-source postgres \
--pipeline orders \
--name app_db \
--host db.internal \
--user skippr_reader \
--password '${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_schemafrom 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 dbt if you want Git-reviewed SQL models on a warehouse (or DuckDB Iceberg in v2), and Lake Compute staying beta is fine.
Choose Skippr if you want one binary, live SQL on Iceberg ∪ WAL, and files that stay yours. Skippr will not compile dbt models.
Install from Install. Connector and query reference live in Developer Docs. For optional onboarding in your VPC, Enterprise Jumpstart or Contact.