Snowflake Architecture Variants

Overview

Three validated architecture blueprints for Snowflake deployments: traditional data warehouse, lakehouse with Iceberg, and data mesh with data sharing.

Variant A: Traditional Data Warehouse

Best for: Single team, centralized analytics, < 50 users

┌──────────────────────────┐
│   Snowflake Account      │
│                          │
│  ┌────────┐  ┌────────┐  │
│  │ Bronze │→ │ Silver │→ Gold │
│  └────────┘  └────────┘       │
│                               │
│  ┌─────────────────────┐      │
│  │ Single ETL Warehouse │      │
│  └─────────────────────┘      │
│                               │
│  ┌──────────┐ ┌──────────┐   │
│  │ BI Tools │ │ Analysts │   │
│  └──────────┘ └──────────┘   │
└──────────────────────────────┘

-- Simple single-account setup
CREATE DATABASE DW;
CREATE SCHEMA DW.RAW;
CREATE SCHEMA DW.CURATED;
CREATE SCHEMA DW.ANALYTICS;

CREATE WAREHOUSE ETL_WH WAREHOUSE_SIZE = 'MEDIUM' AUTO_SUSPEND = 120;
CREATE WAREHOUSE QUERY_WH WAREHOUSE_SIZE = 'SMALL' AUTO_SUSPEND = 60;

Variant B: Lakehouse with Iceberg Tables

Best for: Hybrid cloud/on-prem, existing data lake, open table format requirement

┌──────────────────────┐     ┌─────────────────────┐
│   External Storage   │     │  Snowflake Account   │
│   (S3/GCS/Azure)     │     │                      │
│                      │     │  ┌────────────────┐   │
│  ┌─────────────┐     │←───→│  │ Iceberg Tables │   │
│  │ Parquet/    │     │     │  │ (managed)      │   │
│  │ Iceberg     │     │     │  └────────────────┘   │
│  │ files       │     │     │                      │
│  └─────────────┘     │     │  ┌────────────────┐   │
│                      │     │  │ Native Tables  │   │
│  ┌─────────────┐     │     │  │ (hot data)     │   │
│  │ Spark/Flink │     │     │  └────────────────┘   │
│  │ (external)  │     │     │                      │
│  └─────────────┘     │     │  ┌────────────────┐   │
└──────────────────────┘     │  │ Dynamic Tables │   │
                             │  │ (transforms)   │   │
                             │  └────────────────┘   │
                             └──────────────────────┘

-- Iceberg table backed by external storage
CREATE ICEBERG TABLE events_iceberg (
  event_id STRING,
  event_type STRING,
  event_data VARIANT,
  event_timestamp TIMESTAMP_NTZ
)
  CATALOG = 'SNOWFLAKE'
  EXTERNAL_VOLUME = 'my_s3_volume'
  BASE_LOCATION = 'iceberg/events/';

-- External volume for S3
CREATE EXTERNAL VOLUME my_s3_volume
  STORAGE_LOCATIONS = (
    (NAME = 'primary'
     STORAGE_BASE_URL = 's3://my-data-lake/'
     STORAGE_PROVIDER = 'S3'
     STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::123456789:role/snowflake-iceberg')
  );

-- Dynamic Iceberg table for transforms (writes back to your storage)
CREATE DYNAMIC ICEBERG TABLE curated_events
  TARGET_LAG = '30 minutes'
  WAREHOUSE = ETL_WH
  CATALOG = 'SNOWFLAKE'
  EXTERNAL_VOLUME = 'my_s3_volume'
  BASE_LOCATION = 'iceberg/curated_events/'
AS
  SELECT event_id, event_type, event_data,
         event_timestamp, CURRENT_TIMESTAMP() AS processed_at
  FROM events_iceberg
  WHERE event_type IS NOT NULL;

Variant C: Data Mesh with Data Sharing

Best for: Multi-team, multi-account, decentralized ownership

┌─────────────────┐   ┌─────────────────┐   ┌─────────────────┐
│  Finance Account │   │  Marketing Acct  │   │  Engineering    │
│                  │   │                  │   │  Account        │
│  ┌────────────┐  │   │  ┌────────────┐  │   │  ┌────────────┐ │
│  │ Finance DB │  │   │  │ Marketing  │  │   │  │ Product DB │ │
│  │ (owner)    │──┼──→│  │ DB (owner) │──┼──→│  │ (owner)    │ │
│  └────────────┘  │   │  └────────────┘  │   │  └────────────┘ │
│                  │   │                  │   │                 │
│  ┌────────────┐  │   │  ┌────────────┐  │   │  ┌────────────┐ │
│  │ Shared:    │  │   │  │ Shared:    │  │   │  │ Shared:    │ │
│  │ Product,   │←─┼───┼──│ Finance    │←─┼───┼──│ Marketing, │ │
│  │ Marketing  │  │   │  │ Product    │  │   │  │ Finance    │ │
│  └────────────┘  │   │  └────────────┘  │   │  └────────────┘ │
└─────────────────┘   └─────────────────┘   └─────────────────┘

-- PROVIDER: Create a share from Finance account
CREATE SHARE finance_share;
GRANT USAGE ON DATABASE FINANCE_DW TO SHARE finance_share;
GRANT USAGE ON SCHEMA FINANCE_DW.GOLD TO SHARE finance_share;

-- Only share secure views (hides underlying SQL)
CREATE SECURE VIEW FINANCE_DW.GOLD.REVENUE_SUMMARY AS
  SELECT region, product_line,
         SUM(revenue) AS total_revenue,
         COUNT(DISTINCT customer_id) AS customer_count
  FROM FINANCE_DW.SILVER.TRANSACTIONS
  GROUP BY region, product_line;

GRANT SELECT ON VIEW FINANCE_DW.GOLD.REVENUE_SUMMARY TO SHARE finance_share;

-- Add consumer accounts
ALTER SHARE finance_share ADD ACCOUNTS = myorg.marketing_account, myorg.engineering_account;

-- CONSUMER: Create database from share
CREATE DATABASE FINANCE_SHARED FROM SHARE myorg.finance_account.finance_share;
-- Zero-copy, real-time, no data movement

-- Query shared data as if it's local
SELECT * FROM FINANCE_SHARED.GOLD.REVENUE_SUMMARY
WHERE region = 'North America';

Variant D: Snowpark-Native Application

Best for: ML/AI workloads, Python-heavy teams, stored procedure logic

# Snowpark Python — run Python natively inside Snowflake
from snowflake.snowpark import Session
from snowflake.snowpark.functions import col, sum as sf_sum, avg

# Create session
session = Session.builder.configs({
    "account": os.environ['SNOWFLAKE_ACCOUNT'],
    "user": os.environ['SNOWFLAKE_USER'],
    "password": os.environ['SNOWFLAKE_PASSWORD'],
    "warehouse": "ML_WH",
    "database": "PROD_DW",
    "schema": "GOLD",
}).create()

# DataFrame API (lazy evaluation, pushdown to Snowflake)
orders_df = session.table("orders")
revenue = (
    orders_df
    .filter(col("order_date") >= "2026-01-01")
    .group_by("customer_id")
    .agg(
        sf_sum("amount").alias("total_spend"),
        avg("amount").alias("avg_order"),
    )
    .filter(col("total_spend") > 1000)
    .sort(col("total_spend").desc())
)
revenue.show()  # Executes in Snowflake, not locally

# Register as stored procedure (runs inside Snowflake)
@session.sproc(name="train_model", replace=True, is_permanent=True,
               stage_location="@ML_STAGE", packages=["scikit-learn"])
def train_model(session: Session, table_name: str) -> str:
    df = session.table(table_name).to_pandas()
    from sklearn.ensemble import RandomForestClassifier
    model = RandomForestClassifier()
    model.fit(df[['feature1', 'feature2']], df['label'])
    return f"Trained on {len(df)} rows, score: {model.score(...)}"

# Register UDF
@session.udf(name="predict_churn", replace=True, is_permanent=True,
             stage_location="@ML_STAGE")
def predict_churn(tenure: int, monthly_charge: float) -> float:
    # Model loaded from stage at runtime
    return model.predict_proba([[tenure, monthly_charge]])[0][1]

Decision Matrix

Error Handling

Resources

• Data Sharing
• Iceberg Tables
• Snowpark Python
• Secure Views

Next Steps

For common anti-patterns, see snowflake-known-pitfalls.