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dbt Seed Integration

misata dbt-seed generates synthetic data and writes one CSV file per table directly into your dbt seeds/ directory. Run dbt seed afterwards and your data is in the warehouse.

Quick start

# Generate SaaS seed data into dbt's seeds directory
misata dbt-seed --story "A SaaS company with 1k users" --seeds-dir seeds/

# Then load into your warehouse
dbt seed

Output:

✓ users         — 1,000 rows → seeds/users.csv
✓ subscriptions — 1,200 rows → seeds/subscriptions.csv
✓ invoices      — 3,500 rows → seeds/invoices.csv

Run dbt seed to load 3 table(s) into your warehouse.

Options

Flag Default Description
--story, -s — Plain-English dataset description
--config, -c — Path to a misata.yaml schema file
--seeds-dir seeds/ dbt seeds directory
--rows, -n 1000 Row count for the primary table
--seed 42 Random seed for reproducibility
--force False Overwrite existing CSV files

Use a YAML schema

For precise control over column types and distributions, point to a misata.yaml:

misata dbt-seed --config misata.yaml --seeds-dir dbt/seeds/ --rows 5000

1. Generate seeds during development

# In your dbt project root
misata dbt-seed \
  --story "An ecommerce store with customers, products, and orders" \
  --seeds-dir seeds/ \
  --rows 2000

2. Add seeds to dbt_project.yml

# dbt_project.yml
seeds:
  my_project:
    customers:
      +column_types:
        customer_id: bigint
        created_at: timestamp
    orders:
      +column_types:
        order_id: bigint
        amount: numeric(10,2)

3. Load into your warehouse

dbt seed
dbt run
dbt test

Regenerating seeds

Seeds are reproducible — same --seed value produces identical data:

# Regenerate with the same data (idempotent)
misata dbt-seed --story "A SaaS company" --rows 1000 --seed 42 --force

# Generate a different dataset variant
misata dbt-seed --story "A SaaS company" --rows 1000 --seed 99 --force

CI/CD integration

Generate seeds as part of your CI pipeline before running dbt test:

# .github/workflows/dbt.yml
- name: Generate synthetic seed data
  run: |
    pip install misata
    misata dbt-seed \
      --story "An ecommerce store with customers and orders" \
      --seeds-dir seeds/ \
      --rows 500 \
      --force

- name: Run dbt
  run: |
    dbt seed
    dbt run
    dbt test

Python API alternative

If you need more control, use the Python API directly:

import misata
import pandas as pd
from pathlib import Path

tables = misata.generate("An ecommerce store with 2k customers", rows=2000, seed=42)

seeds_dir = Path("seeds/")
seeds_dir.mkdir(exist_ok=True)

for table_name, df in tables.items():
    df.to_csv(seeds_dir / f"{table_name}.csv", index=False)
    print(f"Written: {table_name}.csv ({len(df):,} rows)")