Snowpark is Snowflake's developer framework that allows you to write data processing logic in Python, Java, or Scala that executes natively inside Snowflake's compute engine. Instead of extracting data to an external cluster (like Spark), Snowpark brings the code to the data — eliminating data movement and leveraging Snowflake's elastic compute.
Why Snowpark?
Before Snowpark, working with Snowflake meant:
- SQL only: Complex transformations were difficult in pure SQL
- External processing: Move data to Spark/Python, process, move back
- Data movement costs: Egress fees + latency + security risks
Snowpark says: Write Python/Java/Scala code that runs inside Snowflake.
``python
from snowflake.snowpark import Session
from snowflake.snowpark.functions import col, avg
session = Session.builder.configs(connection_params).create()
# DataFrame API — runs inside Snowflake
df = session.table('sales')
result = df.filter(col('year') == 2025) \
.group_by('region') \
.agg(avg('revenue').alias('avg_revenue'))
result.show()
``
Key Components
Snowpark DataFrames
- Lazy evaluation — operations build a query plan
- Optimized by Snowflake's SQL optimizer
- Similar API to PySpark DataFrames
- Zero data movement — everything runs in Snowflake
Snowpark Python UDFs
- Write custom Python functions, deploy as UDFs
- Import Python packages (pandas, scikit-learn, xgboost)
- Vectorized UDFs for high-performance batch processing
Snowpark ML
- Train ML models inside Snowflake
- Feature engineering with Snowpark DataFrames
- Model registry for versioning and deployment
- Inference at scale without data movement
Stored Procedures
- Write complex logic in Python/Java/Scala
- Schedule with Snowflake Tasks
- Handle orchestration natively
Snowpark vs PySpark
| Feature | Snowpark | PySpark |
|---------|----------|---------|
| Compute | Snowflake warehouses | Spark clusters |
| Data Location | In Snowflake | In data lake/cluster |
| Infrastructure | Zero (serverless) | Cluster management |
| Language | Python, Java, Scala | Python, Java, Scala, R |
| Best For | Snowflake-native workloads | Data lake processing |
Common Use Cases
1. ML in Snowflake: Train and deploy models without moving data
2. Complex ETL: Python-based transformations beyond SQL capabilities
3. Feature Engineering: Build ML features using DataFrame API
4. Data Apps: Build data applications with Streamlit in Snowflake
5. UDF Libraries: Create reusable Python functions across the org