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Skippr Cloud ELTExtract, load, and model — with reviewable dbt.

The Skippr runner is Cloud ELT for implementers: discover sources, sync into your warehouse, and generate silver/gold dbt you can inspect and own.

Install
Cloud APIs
GitHub

Fast first pipeline

Install the runner, connect a source and destination, and materialize bronze, silver, and gold assets without hand-authoring the first dbt project.

Deterministic schema handling

Schema discovery and type mapping are deterministic, so ingestion correctness does not depend on model output.

Reviewable dbt output

Skippr writes standard dbt files you can inspect, edit, test, and run in your own warehouse workflows.

AI where it helps

AI assists with model and test scaffolding. Schema metadata is the default input, and data samples are optional and off by default.

Final-state CDC

Supported CDC paths use order tokens, tombstones, and warehouse reconciliation to converge on the correct final table state.

Data path stays yours

Authentication and control-plane services are Cloud-backed. Row-level source data moves from the machine running `skippr` to your destination, not through a Skippr-hosted data plane.

Status: Preview — the runner, connectors, and Cloud control plane are available for evaluation. Cloud lake ingest APIs (CreatePipeline / StartRun) are documented on Cloud ELT APIs.

Install ​

See the Install guide for the full setup, including Windows PowerShell.

curl -fsSL https://install.skippr.io/install.sh | shClick to copy

Installing Skippr means accepting the Skippr EULA.

Skippr is a compiled runner. For the first pipeline you also need Python 3.10+, dbt-core, and the adapter for your destination because Skippr generates and validates standard dbt output.

See Install for the full setup.

Choose an evaluation path ​

PathBest forGuide
SnowflakeProduction-style evaluation with a warehouse most teams already useQuick Start: Snowflake
PostgreSQLLocal evaluation without a cloud warehouse accountQuick Start: PostgreSQL
BigQueryGCP-first evaluation pathQuick Start: BigQuery

What Cloud ELT does ​

  1. Discover — reads source metadata and determines the destination shape.
  2. Sync — moves raw data into your configured destination.
  3. Model — drafts and validates silver and gold dbt assets for review.
  4. Validate — compiles and runs the generated project against your destination (skippr test for dbt tests; the modeling workflow also performs validation steps).

See How It Works for the full pipeline flow and how it maps to the CLI phases.

Data and service boundaries ​

  • Data path: source data moves from the machine running skippr to your destination. It is not sent through Skippr Cloud as a hosted data plane.
  • AI input by default: schema metadata is the default model input. Data samples are optional and off by default.
  • Cloud-backed services: authentication, hosted LLM access by default, and control-plane services are Cloud-backed.
  • Reviewable output: the result is your warehouse tables plus standard dbt files you can inspect and extend.

Architecture and deep dives ​

Connectors ​

Cloud ELT supports databases, warehouses, object stores, streaming systems, and operational sinks. Strong evaluation starting points: Snowflake, PostgreSQL, BigQuery, MSSQL, S3, MySQL, MongoDB, DynamoDB, and Kafka.

Connector guides include authentication, permissions or network requirements, and troubleshooting.

See Source Connectors and Destination Connectors.

Requirements ​

DependencyWhy
Python 3.10+Required by dbt
dbt-core + warehouse adapterModel compilation and materialisation
A Skippr Cloud accountAuthentication and Cloud-backed control-plane services