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This integration is ideal for data engineers who need to enrich large datasets with web intelligence directly in their Snowflake pipelines—without leaving SQL or building custom API integrations. Parallel provides a SQL-native User Defined Table Function (UDTF) for Snowflake that enables data enrichment directly in your SQL queries. The integration uses Snowflake’s External Access feature to securely connect to the Parallel API, and batches all rows in a partition into a single API call for efficient processing.
View the complete demo notebook:

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Features

  • SQL-Native: Use parallel_enrich() directly in Snowflake SQL queries
  • Batched Processing: All rows in a partition are sent in a single API call using end_partition()
  • Secure: API key stored as Snowflake Secret, accessed via External Access Integration
  • Configurable Processors: Choose from lite-fast to pro for speed vs thoroughness tradeoffs
  • Structured Output: Returns VARIANT columns for input and enriched data

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Installation

pip install parallel-web-tools[snowflake]
The standalone parallel-cli binary does not include deployment commands. You must install via pip with the [snowflake] extra to deploy the Snowflake integration.

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Deployment

The Snowflake integration requires a one-time deployment step to set up the External Access Integration, secrets, and UDTF in your Snowflake account.

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Prerequisites

  1. Snowflake Account - Paid account required (trial accounts don’t support External Access)
  2. ACCOUNTADMIN Role - Required for creating External Access Integrations
  3. Parallel API Key from Parallel

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Finding Your Account Identifier

Your Snowflake account identifier is in your Snowsight URL: 
https://app.snowflake.com/ORGNAME/ACCOUNTNAME/worksheets
                         └───────┬───────────┘
                     Account: ORGNAME-ACCOUNTNAME

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Deploy with CLI

parallel-cli enrich deploy --system snowflake \
    --account ORGNAME-ACCOUNTNAME \
    --user your-username \
    --password "your-password" \
    --warehouse COMPUTE_WH
If your account requires MFA: 
parallel-cli enrich deploy --system snowflake \
    --account ORGNAME-ACCOUNTNAME \
    --user your-username \
    --password "your-password" \
    --authenticator username_password_mfa \
    --passcode 123456 \
    --warehouse COMPUTE_WH
This creates:
  • Database: PARALLEL_INTEGRATION
  • Schema: ENRICHMENT
  • Network rule for api.parallel.ai
  • Secret with your API key
  • External Access Integration
  • parallel_enrich() UDTF (batched table function)
  • Roles: PARALLEL_DEVELOPER and PARALLEL_USER
For manual deployment options (useful if you don’t have ACCOUNTADMIN), troubleshooting, MFA setup, and cleanup instructions, see the complete Snowflake setup guide.

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Basic Usage

The parallel_enrich() function is a table function (UDTF) that requires the TABLE(...) OVER (PARTITION BY ...) syntax: 
WITH companies AS (
    SELECT * FROM (VALUES
        ('Google', 'google.com'),
        ('Anthropic', 'anthropic.com'),
        ('Apple', 'apple.com')
    ) AS t(company_name, website)
)
SELECT
    e.input:company_name::STRING AS company_name,
    e.input:website::STRING AS website,
    e.enriched:ceo_name::STRING AS ceo_name,
    e.enriched:founding_year::STRING AS founding_year
FROM companies t,
     TABLE(PARALLEL_INTEGRATION.ENRICHMENT.parallel_enrich(
         TO_JSON(OBJECT_CONSTRUCT('company_name', t.company_name, 'website', t.website)),
         ARRAY_CONSTRUCT('CEO name', 'Founding year')
     ) OVER (PARTITION BY 1)) e;
Output:
company_namewebsiteceo_namefounding_year
Googlegoogle.comSundar Pichai1998
Anthropicanthropic.comDario Amodei2021
Appleapple.comTim Cook1976

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Function Parameters

ParameterTypeDescription
input_jsonVARCHARJSON string via TO_JSON(OBJECT_CONSTRUCT(...))
output_columnsARRAYArray of descriptions for columns you want to enrich
processorVARCHAR(Optional) Processor to use (default: lite-fast)

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Return Values

The function returns a table with two VARIANT columns:
ColumnDescription
inputOriginal input data as VARIANT
enrichedEnrichment results including basis citations
The enriched column contains: 
{
  "ceo_name": "Sundar Pichai",
  "founding_year": "1998",
  "basis": [{"field": "ceo_name", "citations": [...], "confidence": "high"}]
}
Field names are automatically converted to snake_case (e.g., “CEO name” → ceo_name).

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Custom Processor

Override the default processor by adding a third parameter: 
SELECT
    e.input:company_name::STRING AS company_name,
    e.enriched:ceo_name::STRING AS ceo_name
FROM companies t,
     TABLE(PARALLEL_INTEGRATION.ENRICHMENT.parallel_enrich(
         TO_JSON(OBJECT_CONSTRUCT('company_name', t.company_name)),
         ARRAY_CONSTRUCT('CEO name'),
         'base-fast'  -- processor option
     ) OVER (PARTITION BY 1)) e;

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Batching with PARTITION BY

The PARTITION BY clause controls how rows are batched into API calls. All rows in the same partition are sent together in a single API request.

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All Rows in One Batch

-- Single API call for all rows (fastest for small datasets)
TABLE(parallel_enrich(...) OVER (PARTITION BY 1))

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Batch by Column

-- One API call per region
SELECT
    e.input:company_name::STRING AS company_name,
    e.enriched:ceo_name::STRING AS ceo_name
FROM companies t,
     TABLE(PARALLEL_INTEGRATION.ENRICHMENT.parallel_enrich(
         TO_JSON(OBJECT_CONSTRUCT('company_name', t.company_name, 'region', t.region)),
         ARRAY_CONSTRUCT('CEO name')
     ) OVER (PARTITION BY t.region)) e;

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Fixed Batch Sizes

-- Process in batches of 100 rows
WITH numbered AS (
    SELECT *, CEIL(ROW_NUMBER() OVER (ORDER BY company_name) / 100.0) AS batch_id
    FROM companies
)
SELECT
    e.input:company_name::STRING AS company_name,
    e.enriched:ceo_name::STRING AS ceo_name
FROM numbered t,
     TABLE(PARALLEL_INTEGRATION.ENRICHMENT.parallel_enrich(
         TO_JSON(OBJECT_CONSTRUCT('company_name', t.company_name)),
         ARRAY_CONSTRUCT('CEO name')
     ) OVER (PARTITION BY t.batch_id)) e;

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Choosing a Partition Strategy

PatternUse Case
PARTITION BY 1Small datasets (under 1000 rows), fastest for few rows
PARTITION BY columnLarge datasets, natural groupings, incremental processing
PARTITION BY batch_idFixed batch sizes for very large datasets

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Processor Selection

Choose a processor based on your speed vs thoroughness requirements. See Choose a Processor for detailed guidance and Pricing for cost information.
ProcessorSpeedBest For
lite-fastFastestBasic metadata, high volume
base-fastFastStandard enrichments
core-fastMediumCross-referenced data
pro-fastSlowerDeep research

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Best Practices

For smaller datasets, batch all rows together for maximum efficiency:
TABLE(parallel_enrich(...) OVER (PARTITION BY 1))
Be specific in your output column descriptions for better results:
ARRAY_CONSTRUCT(
    'CEO name (current CEO or equivalent leader)',
    'Founding year (YYYY format)'
)
Store enriched results in a table to avoid re-processing:
CREATE TABLE enriched_companies AS
SELECT
    e.input:company_name::STRING AS company_name,
    e.enriched:ceo_name::STRING AS ceo_name,
    e.enriched:founding_year::STRING AS founding_year
FROM companies t,
     TABLE(PARALLEL_INTEGRATION.ENRICHMENT.parallel_enrich(
         TO_JSON(OBJECT_CONSTRUCT('company_name', t.company_name)),
         ARRAY_CONSTRUCT('CEO name', 'Founding year')
     ) OVER (PARTITION BY 1)) e;
Process new records daily using date partitioning:
SELECT e.*
FROM companies t,
     TABLE(PARALLEL_INTEGRATION.ENRICHMENT.parallel_enrich(
         TO_JSON(OBJECT_CONSTRUCT('company_name', t.company_name)),
         ARRAY_CONSTRUCT('CEO name')
     ) OVER (PARTITION BY DATE_TRUNC('day', t.created_at))) e
WHERE t.created_at >= CURRENT_DATE;

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Security

The integration uses Snowflake’s security features:
  1. Network Rule: Only allows egress to api.parallel.ai:443
  2. Secret: API key stored encrypted (not visible in SQL)
  3. External Access Integration: Combines network rule and secret
  4. Roles: PARALLEL_USER for query access, PARALLEL_DEVELOPER for UDF management
Grant access to users: 
GRANT ROLE PARALLEL_USER TO USER analyst_user;