Quick Start: PostgreSQL

Six commands to go from files in S3 to materialised dbt models in PostgreSQL -- bronze, silver, and gold layers, all generated and validated automatically. No cloud warehouse account required.

Prerequisites

• skippr on PATH (Install)
• Python venv with dbt-core and dbt-postgres
• Authenticated via skippr user login (or SKIPPR_API_KEY for CI)
• PostgreSQL running locally or remotely
• PostgreSQL and AWS credentials in your environment:

export POSTGRES_HOST="localhost"
export POSTGRES_USER="myuser"
export POSTGRES_PASSWORD="mypassword"
export AWS_ACCESS_KEY_ID="AKIA..."
export AWS_SECRET_ACCESS_KEY="..."

Need help with credentials? See PostgreSQL and S3.

Build the pipeline

# 1. Create the project
mkdir my-workspace && cd my-workspace
skippr init s3-pipeline

# 2. Point at your warehouse
skippr connect warehouse postgres \
  --database analytics \
  --schema raw

# 3. Point at your source
skippr connect source s3 \
  --bucket my-data-bucket \
  --prefix raw/

# 4. Verify everything is wired up
skippr doctor

# 5. Load bronze data
skippr sync --pipeline s3-pipeline --once

# 6. Generate and validate dbt models
skippr model --pipeline s3-pipeline

That's it. skippr sync --pipeline s3-pipeline --once discovers file schemas when needed and loads data into PostgreSQL; skippr model --pipeline s3-pipeline then generates a complete dbt project with silver and gold models -- compiled and materialised.

What you get

dbt models (ready to extend)

models/
├── schema.yml                   # source definitions
└── staging/
    ├── stg_raw_events.sql       # silver model
    └── stg_raw_sessions.sql     # silver model

PostgreSQL schemas (populated and queryable)

Schema	Contents
raw	Bronze -- raw extracted data
s3_pipeline_silver	Silver -- staged and cleansed
s3_pipeline_gold	Gold -- mart-ready models

Project config

# skippr.yml
skippr:
  workspace: s3_pipeline

pipelines:
  s3-pipeline:
    data_source: data_sources.source
    data_sink: data_sinks.warehouse

data_sources:
  source:
    S3:
      s3_bucket: my-data-bucket
      s3_prefix: raw/

data_sinks:
  warehouse:
    Postgres:
      database: analytics
      schema: raw

What you have at the end

• You can evaluate Skippr without a cloud warehouse account.
• The runner reads S3 data and writes it directly into PostgreSQL.
• Skippr generates a reviewable dbt project as standard dbt files you can inspect and extend.
• For the full pipeline flow and architecture, see How It Works and Core Concepts.

What's next

• Run skippr sync --pipeline s3-pipeline --once again -- it is incremental, only new and changed rows are synced.
• Re-run skippr model --pipeline s3-pipeline to resume the latest modeling thread. Use skippr model --pipeline s3-pipeline --no-resume when you want a clean modeling attempt.
• The dbt project is yours. Add tests, snapshots, or custom gold models.
• See How It Works for the full pipeline breakdown.
• Ready for production? Swap to Snowflake or any of the supported warehouses with a single command.