CLI Reference

databar enrich list
databar enrich list --query "linkedin"
databar enrich list --format json

# Shows parameters, response fields, pricing
databar enrich get 123
databar enrich get 123 --format json

# Submits and polls until complete
databar enrich run 123 --params '{"email": "alice@example.com"}'

# JSON output (pipe-friendly)
databar enrich run 123 --params '{"email": "alice@example.com"}' --format json

# Raw result without formatting
databar enrich run 123 --params '{"email": "alice@example.com"}' --raw

# Input CSV must have column headers matching enrichment param names
databar enrich bulk 123 --input leads.csv
databar enrich bulk 123 --input leads.csv --format csv --out results.csv

# For select/multiselect parameters
databar enrich choices 123 country
databar enrich choices 123 country --query "united"
databar enrich choices 123 country --page 2 --limit 100

databar waterfall list
databar waterfall list --query "email"
databar waterfall list --format json

databar waterfall get email_getter
databar waterfall get email_getter --format json

# Uses all available providers by default
databar waterfall run email_getter \
  --params '{"linkedin_url": "https://linkedin.com/in/alice"}'

# Specify providers explicitly (comma-separated IDs)
databar waterfall run email_getter \
  --params '{"linkedin_url": "https://linkedin.com/in/alice"}' \
  --providers 10,11

# With email verification
databar waterfall run email_getter \
  --params '{"linkedin_url": "https://linkedin.com/in/alice"}' \
  --email-verifier 99

databar waterfall bulk email_getter --input leads.csv
databar waterfall bulk email_getter --input leads.csv --out results.csv

databar table list
databar table list --format json

# Create empty table
databar table create --name "My Leads"

# Create with predefined columns
databar table create --name "My Leads" --columns "email,name,company,linkedin_url"

# List columns
databar table columns <uuid>
databar table columns <uuid> --format json

# Get rows
databar table rows <uuid>
databar table rows <uuid> --page 2 --per-page 500
databar table rows <uuid> --format csv --out rows.csv

# From inline JSON array
databar table insert <uuid> \
  --data '[{"email":"alice@example.com","name":"Alice"}]'

# From CSV file
databar table insert <uuid> --input data.csv

# Auto-create unknown columns
databar table insert <uuid> --input data.csv --allow-new-columns

# With deduplication
databar table insert <uuid> --input data.csv --dedupe-keys email

# Multiple dedupe keys
databar table insert <uuid> --input data.csv --dedupe-keys "email,linkedin_url"

# Rows must include an "id" field (the row UUID)
databar table patch <uuid> \
  --data '[{"id":"row-uuid","name":"Updated Name"}]'

# From CSV (must have "id" column)
databar table patch <uuid> --input updates.csv

# Only fill empty cells (don't overwrite existing values)
databar table patch <uuid> --input updates.csv --no-overwrite

# Insert or update matched by key column
databar table upsert <uuid> --key-col email \
  --data '[{"email":"alice@example.com","name":"Alice"}]'

# From CSV
databar table upsert <uuid> --key-col email --input data.csv

# List enrichments configured on a table
databar table enrichments <uuid>

# Add an enrichment to a table
databar table add-enrichment <uuid> \
  --enrichment-id 123 \
  --mapping '{"email": "email_column"}'

# Run an enrichment on all table rows
databar table run-enrichment <uuid> --enrichment-id <table-enrichment-id>

# Run only on empty rows
databar table run-enrichment <uuid> --enrichment-id <id> --run-strategy empty_only

databar task get <task-id>
databar task get <task-id> --format json

# Blocks until the task finishes or times out (~5 minutes)
databar task get <task-id> --poll
databar task get <task-id> --poll --format json