semantica/docs/integrations/snowflake.md

title, description, icon

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Snowflake Integration	Ingest structured data from Snowflake tables and queries into Semantica's KG pipeline.	snowflake

Extract data from Snowflake into Semantica with password, key-pair, OAuth, and SSO authentication.

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Installation

# Install with Snowflake support
pip install "semantica[db-snowflake]"

# Or install the connector separately
pip install snowflake-connector-python

---

Basic Usage

from semantica.ingest import SnowflakeIngestor
import os

ingestor = SnowflakeIngestor(
    account=os.getenv("SNOWFLAKE_ACCOUNT"),
    user=os.getenv("SNOWFLAKE_USER"),
    password=os.getenv("SNOWFLAKE_PASSWORD"),
    warehouse=os.getenv("SNOWFLAKE_WAREHOUSE"),
    database=os.getenv("SNOWFLAKE_DATABASE"),
    schema=os.getenv("SNOWFLAKE_SCHEMA"),
)

data = ingestor.ingest_table("CUSTOMERS")
print(f"Retrieved {data.row_count} rows — columns: {data.columns}")

Use environment variables (or a `.env` file with `python-dotenv`) to keep credentials out of source code. `SnowflakeIngestor()` with no arguments reads from `SNOWFLAKE_*` environment variables automatically.

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Authentication Methods

```python ingestor = SnowflakeIngestor( account="myaccount", user="myuser", password="mypassword", warehouse="COMPUTE_WH", ) ``` ```python ingestor = SnowflakeIngestor( account="myaccount", user="myuser", private_key_path="/path/to/rsa_key.p8", warehouse="COMPUTE_WH", ) ``` Preferred for production — no password stored in config. ```python ingestor = SnowflakeIngestor( account="myaccount", user="myuser", authenticator="oauth", token="your_oauth_token", warehouse="COMPUTE_WH", ) ``` ```python ingestor = SnowflakeIngestor( account="myaccount", user="myuser", authenticator="externalbrowser", warehouse="COMPUTE_WH", ) ```

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Querying

Ingest a table with filters

data = ingestor.ingest_table(
    "CUSTOMERS",
    where="COUNTRY = 'USA' AND CREATED_DATE > '2024-01-01'",
    order_by="CREATED_DATE DESC",
    limit=10000,
)

Custom SQL

data = ingestor.ingest_query("""
    SELECT CUSTOMER_ID, SUM(AMOUNT) AS TOTAL_AMOUNT
    FROM SALES
    WHERE DATE >= '2024-01-01'
    GROUP BY CUSTOMER_ID
""")

Schema introspection

schema = ingestor.get_table_schema("CUSTOMERS")
for column in schema["columns"]:
    print(f"{column['name']}: {column['type']}")

---

Export as Semantica Documents

documents = ingestor.export_as_documents(
    data,
    id_field="CUSTOMER_ID",
    text_fields=["NAME", "EMAIL", "NOTES"],
)
print(f"Created {len(documents)} documents for processing")

---

Batch Processing Large Tables

PAGE_SIZE = 5000
for page in range(total_pages):
    data = ingestor.ingest_table(
        "LARGE_TABLE",
        limit=PAGE_SIZE,
        offset=page * PAGE_SIZE,
    )
    process_batch(data)

Or use the built-in batch_size parameter:

data = ingestor.ingest_query(
    "SELECT * FROM LARGE_TABLE",
    batch_size=5000,
)

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Troubleshooting

from semantica.ingest import SnowflakeConnector

connector = SnowflakeConnector(account="myaccount", user="myuser", password="mypassword")
if not connector.test_connection():
    print("Connection failed — check credentials and account identifier")

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See Also

Full SnowflakeIngestor and all other ingestors. Use Snowflake ingestion as a pipeline step. All optional dependency extras. Build a KG from ingested Snowflake data.