domo-data-generator

name

domo-data-generator

description

**Generating sample data for Domo** -- invoke when a user needs to create realistic sample datasets and upload them to a Domo instance. Primary signals: requests for sample data, demo data, test data, fake data for Domo; mentions of Salesforce, Google Analytics, QuickBooks, NetSuite, Google Ads, Facebook Ads, HubSpot, Marketo, or Health Portal sample data; questions about the datagen CLI or domo_data_generator. Covers: generating datasets, uploading to Domo, creating datasets in Domo, rolling dates, entity pools, connector icons, catalog management, and adding new dataset definitions. Skip for: real connector setup, production data pipelines, data transformations (Magic ETL), or Domo App Platform.

Domo Sample Data Generator

Generate realistic, cross-referenced sample data for Domo using the datagen CLI.

Repository: https://github.com/brrink/domo_data_generator

---

Overview

The generator creates sample data mirroring major business platforms with consistent cross-source entity integrity, then uploads it to Domo. It includes:

• 18 pre-built datasets across 6 source categories (Salesforce, Google Analytics, Financial, Marketing, Health, AdPoint)
• YAML-driven catalog for easy dataset additions
• Shared entity pool (companies, people, products, sales reps, campaigns)
• Date rolling to keep data looking current
• Direct Domo integration (create datasets, upload, set connector icons)
• Structured JSON output by default (AI-agent-friendly)
• pipx installable -- runs from any directory

---

Setup

# Install globally with pipx (recommended)
pipx install git+https://github.com/brrink/domo_data_generator.git

# Initialize a working directory
mkdir my-domo-data && cd my-domo-data
datagen init

# Edit .env with your Domo credentials

If .env.example is missing or you want a clean start, create .env in the working directory with:

cat > .env <<'EOF'
DOMO_CLIENT_ID=your_client_id_here
DOMO_CLIENT_SECRET=your_client_secret_here
DOMO_API_HOST=api.domo.com
DOMO_INSTANCE=your_instance_name
DOMO_SET_CONNECTOR_TYPE=false
EOF

Required Environment Variables

Variable	Purpose
DOMO_CLIENT_ID	OAuth client identifier
DOMO_CLIENT_SECRET	OAuth client secret
DOMO_API_HOST	API endpoint hostname
DOMO_INSTANCE	Domo instance name
DOMO_SET_CONNECTOR_TYPE	Enable connector icon customization (optional, default: false)

Auth boundary note: domo_data_generator uses its own public-API/OAuth credential flow and does not run through community-domo-cli or ryuu session auth.

Current tooling boundary: most Product API automation should use community-domo-cli, but datagen dataset create/upload in this skill currently depends on python -m datagen with .env OAuth credentials (DOMO_CLIENT_ID / DOMO_CLIENT_SECRET).

---

CLI Reference

Entry point: datagen [OPTIONS] COMMAND [ARGS]

Global Options

Option	Description
--verbose / -v	Enable verbose logging
--output / -o TEXT	Output format: json (default), table, yaml
--yes / -y	Skip confirmation prompts

All commands emit structured JSON by default for easy machine parsing.

Init Command

init -- Initialize a working directory

datagen init                  # Initialize current directory
datagen init /path/to/dir     # Initialize a specific directory

Copies bundled catalog YAML files to ./catalog/, creates .env template, and creates ./data/ directory. Run this once before using the CLI in a new directory.

Core Commands

generate -- Generate sample data

datagen generate --all                    # Generate all datasets
datagen generate salesforce_opportunities  # Generate one dataset
datagen generate --all --seed 42          # Reproducible generation
datagen generate --all --dry-run          # Preview without writing

Requires entity pool initialization first. Run python -m datagen pool regenerate before generate even if your schema has no explicit entity_ref columns.

Option	Description
name	Dataset name (YAML filename stem), optional
--all	Generate all datasets
--seed INTEGER	Random seed for reproducibility
--catalog-dir PATH	Catalog directory override
--data-dir PATH	Data directory override
--dry-run	Preview without writing files

upload -- Upload data to Domo (full replace)

datagen upload --all
datagen upload salesforce_opportunities

Requires DOMO_CLIENT_ID and DOMO_CLIENT_SECRET.

Option	Description
name	Dataset name, optional
--all	Upload all datasets
--catalog-dir PATH	Catalog directory override
--data-dir PATH	Data directory override

create-dataset -- Create dataset(s) in Domo from catalog

datagen create-dataset --all --skip-existing
datagen create-dataset salesforce_opportunities

Requires DOMO_CLIENT_ID and DOMO_CLIENT_SECRET. The domo_id is persisted locally (in the catalog YAML if writable, otherwise in data/domo_ids.json).

Option	Description
name	Dataset name, optional
--all	Create all datasets
--skip-existing	Skip datasets that already have a domo_id
--catalog-dir PATH	Catalog directory override

roll-dates -- Shift rolling date columns to stay current

datagen roll-dates
datagen roll-dates --anchor-date 2026-04-01

Option	Description
--anchor-date TEXT	Target date (YYYY-MM-DD), defaults to today
--catalog-dir PATH	Catalog directory override
--data-dir PATH	Data directory override

Informational Commands

list -- List catalog dataset definitions

datagen list                    # JSON output (default)
datagen --output table list     # Rich table for humans
datagen list --verbose          # Include column/schema details

status -- Display generation status for all datasets

datagen status

Connector Icon Commands

Require DOMO_DEVELOPER_TOKEN and DOMO_INSTANCE.

discover-types -- Search Domo connector/provider types

datagen discover-types salesforce

set-type -- Set connector icon on a Domo dataset

datagen set-type salesforce_opportunities
datagen set-type salesforce_opportunities --provider-key custom_key

set-type-all -- Set connector icon on all datasets with a domo_id

datagen set-type-all

Entity Pool Commands

pool regenerate -- Regenerate the shared entity pool

datagen pool regenerate
datagen pool regenerate --seed 99
datagen pool regenerate --company-count 500 --person-count 1000

Option	Default
--seed INTEGER	42
--company-count INTEGER	200
--person-count INTEGER	500
--product-count INTEGER	50
--sales-rep-count INTEGER	20
--campaign-count INTEGER	30

pool show -- Display entity pool summary

datagen pool show

---

Common Workflows

Full setup for a new Domo instance

datagen init
# Edit .env with credentials
datagen pool regenerate
datagen generate --all
datagen create-dataset --all
datagen upload --all
datagen set-type-all

Daily refresh via cron

# Crontab entry: roll dates and re-upload daily at 6 AM
0 6 * * * cd /path/to/project && datagen roll-dates && datagen upload --all

Generate a single dataset end-to-end

datagen generate salesforce_opportunities
datagen create-dataset salesforce_opportunities
datagen upload salesforce_opportunities
datagen set-type salesforce_opportunities

---

Included Datasets

Category	Dataset Name	Key	Rows
Salesforce	Salesforce - Accounts	salesforce_accounts	500
Salesforce	Salesforce - Contacts	salesforce_contacts	1,500
Salesforce	Salesforce - Opportunities	salesforce_opportunities	2,500
Google Analytics	Google Analytics - Sessions	ga_sessions	5,000
Google Analytics	Google Analytics - Page Views	ga_pageviews	10,000
Financial	QuickBooks - Invoices	financial_invoices	3,000
Financial	NetSuite - General Ledger	financial_gl_entries	5,000
Marketing	Google Ads - Campaign Performance	marketing_google_ads	3,000
Marketing	Facebook Ads - Campaign Performance	marketing_facebook_ads	2,500
Marketing	HubSpot - Contacts	marketing_hubspot_contacts	2,000
Marketing	Marketing - Market Leads	marketing_market_leads	2,500
Marketing	Marketo - Leads	marketing_marketo_leads	3,000
Health	Health Portal - Demographics	health_demographics	15
Health	Health Portal - Lab Results	health_lab_results	1,470
Health	Health Portal - Vitals	health_vitals	5,250
AdPoint	AdPoint - Orders	adpoint_orders	150
AdPoint	AdPoint - Line Items	adpoint_line_items	500
AdPoint	AdPoint - Flights	adpoint_flights	2,000

---

Entity Pool

The shared entity pool provides consistent cross-dataset references. Entities are generated once and reused across all datasets.

Entity Type	Default Count	Key Fields
company	200	id, account_id, name, domain, industry, size, city, state, annual_revenue, employee_count
person	500	id, contact_id, first_name, last_name, full_name, email, company_id, company_name, title, phone
product	50	id, name, category, unit_price, sku
sales_rep	20	id, rep_id, first_name, last_name, full_name, email, region
campaign	30	id, name, channel, budget, status

---

Adding New Dataset Definitions

Dataset definitions live in the catalog/ directory as YAML files. Each YAML file defines metadata, columns, and generator configurations.

YAML Structure

dataset:
  name: My Custom Dataset
  domo_id: null
  source_type: custom
  description: "Description of the dataset"
  row_count: 1000
  tags:
    - custom
    - demo

schema:
  - name: id
    type: STRING
    generator: uuid4

  - name: company_name
    type: STRING
    generator: entity_ref
    entity: company
    field: name

  - name: amount
    type: DOUBLE
    generator: random_decimal
    min: 100.0
    max: 10000.0
    precision: 2

  - name: created_date
    type: DATE
    generator: date_range
    start_days_ago: 365
    end_days_ahead: 0
    rolling: true

Available Column Types

STRING, LONG, DOUBLE, DECIMAL, DATETIME, DATE

Available Generators

Generic: uuid4, random_choice, weighted_choice, random_int, random_decimal, date_range, entity_ref, compound, sequence, constant, derived_from_date, stage_derived, faker

Salesforce: sf_id, sf_opportunity_name, sf_case_subject, sf_lead_rating

Google Analytics: ga_session_id, ga_page_path, ga_source, ga_medium, ga_campaign, ga_browser, ga_device_category, ga_country, ga_bounce_rate, ga_session_duration, ga_pageviews, ga_landing_page

Financial: gl_account_code, gl_account_name, invoice_number, payment_terms, payment_method, invoice_status, journal_type, department, fiscal_period, debit_credit

Marketing/Ads: ad_platform, campaign_objective, ad_format, ad_headline, ad_keyword, targeting_type, impressions, clicks_from_impressions, ctr, cost_per_click, ad_spend, conversions_from_clicks, hubspot_lifecycle, hubspot_lead_status, ad_group_id

Health: health_lab_init, health_lab_field, health_vital_init, health_vital_field, health_demographics

Generator Column Options

Option	Used With	Description
entity	entity_ref	Entity pool type to reference
field	entity_ref	Field to pull from the entity
choices	random_choice, weighted_choice	List of possible values
min / max	random_int, random_decimal	Value range
precision	random_decimal	Decimal places
template	compound	String template with {field} placeholders
refs	compound	Column references for template substitution — must be a YAML list of column name strings (e.g. ["sku", "line_id"]), not a dict/object. A dict triggers ValidationError: schema.N.refs — Input should be a valid list.
start_days_ago / end_days_ahead	date_range	Date range relative to today
rolling	date_range	Enable date rolling for freshness
mapping	stage_derived	Map source values to derived values
source_column	stage_derived, derived_from_date	Column to derive from
format	derived_from_date	Date format string
faker_method	faker	Faker library method name
faker_args	faker	Arguments for the Faker method

weighted_choice YAML format:

generator: weighted_choice
choices:
  "Tier 1": 0.40
  "Tier 2": 0.35
  "Tier 3": 0.25

compound refs vs formatted random strings: For values like PO-12345, prefer faker with bothify instead of abusing compound / refs:

- name: purchase_order_ref
  type: STRING
  generator: faker
  faker_method: bothify
  faker_args:
    text: "PO-#####"

---

Rules

1. Run datagen init first -- Initialize a working directory before using any other commands. This copies the catalog and creates .env.
2. Always generate before uploading -- Run generate (or generate --all) before upload to ensure CSV data files exist.
3. Create datasets before first upload -- Run create-dataset before upload for new datasets. The domo_id is persisted locally.
4. Use --skip-existing -- When running create-dataset --all, use --skip-existing to avoid duplicating datasets that already have a domo_id.
5. Entity pool consistency -- Regenerating the pool (pool regenerate) invalidates all previously generated data. Re-generate all datasets afterward.
6. Date rolling -- Use roll-dates before upload to keep date columns current. Only columns with rolling: true are affected.
7. Credentials -- DOMO_CLIENT_ID and DOMO_CLIENT_SECRET are required for upload and create-dataset. DOMO_DEVELOPER_TOKEN is required for set-type and discover-types. Offline commands need no credentials.
8. Reproducibility -- Use --seed for reproducible data generation across runs.
9. Output format -- Default output is JSON. Use --output table for human-readable Rich tables.

---

Checklist

• CLI installed (pipx install git+https://github.com/brrink/domo_data_generator.git)
• Working directory initialized (datagen init)
• .env configured with Domo credentials
• Entity pool generated (datagen pool regenerate)
• Datasets generated (datagen generate --all)
• Datasets created in Domo (datagen create-dataset --all --skip-existing)
• Data uploaded (datagen upload --all)
• Connector icons set (datagen set-type-all) if desired
• Cron configured for daily date rolling and upload if needed