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Documentation Index

Fetch the complete documentation index at: https://docs.databar.ai/llms.txt

Use this file to discover all available pages before exploring further.

Tables let you store structured data in Databar and run enrichments across all rows without managing individual API calls. This is the best approach when you want persistent, viewable results that you can also access in the Databar UI.

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What you will do

  1. Create a table with columns
  2. Insert rows
  3. Find and attach an enrichment
  4. Run the enrichment on all rows
  5. View results in the API or UI

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Prerequisites

  • A Databar API key (get one here)
  • A dataset (names, emails, domains, etc.)

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Step 1: Create a table

curl -X POST "https://api.databar.ai/v1/table/create" \
  -H "x-apikey: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Lead Enrichment",
    "columns": ["name", "company", "domain"]
  }'
Save the uuid from the response. You will use it in every subsequent call.

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Step 2: Insert rows

curl -X POST "https://api.databar.ai/v1/table/TABLE_UUID/rows" \
  -H "x-apikey: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "rows": [
      {"fields": {"name": "Sarah Chen", "company": "Stripe", "domain": "stripe.com"}},
      {"fields": {"name": "James Lee", "company": "Notion", "domain": "notion.so"}},
      {"fields": {"name": "Maria Garcia", "company": "Figma", "domain": "figma.com"}}
    ]
  }'
You can insert up to 100 rows per request.

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Step 3: Find and attach an enrichment

First, find the enrichment you want: 
curl "https://api.databar.ai/v1/enrichments/?q=company%20data" \
  -H "x-apikey: YOUR_API_KEY"
Then attach it to the table with a column mapping that tells Databar which columns to use as input: 
curl -X POST "https://api.databar.ai/v1/table/TABLE_UUID/add-enrichment" \
  -H "x-apikey: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "enrichment": ENRICHMENT_ID,
    "mapping": {
      "domain": {
        "value": "domain",
        "type": "mapping"
      }
    }
  }'
Each mapping key is an enrichment parameter slug. Set type to "mapping" to pull the value from a table column (use the column name or UUID as the value), or "simple" to pass a static value to every row. Save the returned id - this is the table-enrichment ID you will use to run it.

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Step 4: Run the enrichment

curl -X POST "https://api.databar.ai/v1/table/TABLE_UUID/run-enrichment/TABLE_ENRICHMENT_ID" \
  -H "x-apikey: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{}'
This runs the enrichment on all rows. Use "run_strategy": "run_empty" to only process rows that have not been enriched yet.

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Step 5: View results

Fetch the enriched rows: 
curl "https://api.databar.ai/v1/table/TABLE_UUID/rows" \
  -H "x-apikey: YOUR_API_KEY"
Or open the table directly in the Databar UI at https://databar.ai/table/TABLE_UUID.

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With the Python SDK

from databar import DatabarClient

client = DatabarClient()

# Create table and add rows
table = client.create_table(name="Lead Enrichment", columns=["name", "company", "domain"])

client.create_rows(table.uuid, [
    {"name": "Sarah Chen", "company": "Stripe", "domain": "stripe.com"},
    {"name": "James Lee", "company": "Notion", "domain": "notion.so"},
    {"name": "Maria Garcia", "company": "Figma", "domain": "figma.com"},
])

# Attach and run enrichment
te = client.add_enrichment(table.uuid, ENRICHMENT_ID, mapping={
    "domain": {"value": "domain", "type": "mapping"}
})
client.run_table_enrichment(table.uuid, te.id)

# Fetch results
rows = client.get_table_rows(table.uuid)
for row in rows:
    print(row)

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Next steps

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