🎯 The Challenge: Processing TBs of S3 Data Without Breaking the Bank

This content originally appeared on DEV Community and was authored by vinicius fagundes

You've got raw data flooding into S3. CSV files, JSON logs, event streams—all sitting there, waiting to be transformed into something useful and loaded into your Snowflake data warehouse. Traditional approaches mean spinning up EMR clusters, maintaining Spark jobs, or running expensive transformation layers 24/7.

There's a better way. Let's build a serverless data pipeline that processes and prepares data for Snowflake at a fraction of the cost.

🏗️ Architecture Overview

┌─────────────┐     ┌──────────────┐     ┌─────────────┐     ┌──────────────┐     ┌──────────────┐
│  S3 Bucket  │────▶│ Lambda       │────▶│  DuckDB/    │────▶│  S3 Output   │────▶│  Snowflake   │
│  (Raw Data) │     │  (Triggered) │     │  Polars     │     │  (Parquet)   │     │   Warehouse  │
└─────────────┘     └──────────────┘     │  Processing │     └──────────────┘     └──────────────┘
                                          └─────────────┘

💰 Cost Comparison: Why This Matters

Traditional Approach	Serverless Approach	Savings
EMR Cluster: $1,200/month (always-on)	Lambda: $150/month (on-demand)	87%
Fivetran/Airbyte: $2,000/month	DIY Lambda Pipeline: $0/month	100%
EC2 Processing: $800/month	Lambda + DuckDB: $80/month	90%
Total: $4,000/month	Total: $230/month	94%

Note: Snowflake storage and compute costs remain the same in both scenarios

🔧 Option 1: DuckDB + Lambda (Analytics-First)

When to Use:

✅ You need SQL-based transformations
✅ Data is primarily read-heavy (analytics queries)
✅ Files are in columnar formats (Parquet, CSV)
✅ Processing < 50GB per invocation
❌ Not ideal for complex multi-step transformations

Implementation:

# lambda_duckdb_processor.py
import duckdb
import boto3
import os

def lambda_handler(event, context):
    # Get S3 event details
    bucket = event['Records'][0]['s3']['bucket']['name']
    key = event['Records'][0]['s3']['object']['key']

    # Initialize DuckDB with S3 support
    con = duckdb.connect()
    con.execute("""
        INSTALL httpfs;
        LOAD httpfs;
        SET s3_region='us-east-1';
    """)

    # Process directly from S3 - NO DATA MOVEMENT
    query = f"""
        COPY (
            SELECT 
                date_trunc('day', timestamp) as date,
                user_id,
                SUM(revenue) as total_revenue,
                COUNT(*) as transaction_count
            FROM read_parquet('s3://{bucket}/{key}')
            WHERE revenue > 0
            GROUP BY date, user_id
        ) TO 's3://{bucket}/processed/{key}'
        (FORMAT PARQUET, COMPRESSION ZSTD);
    """

    con.execute(query)

    return {
        'statusCode': 200,
        'body': f'Processed {key} successfully'
    }

Performance Characteristics:

File Size    | Processing Time | Lambda Cost | Total Cost
-------------|-----------------|-------------|------------
1 GB         | 12 seconds      | $0.002      | $0.002
10 GB        | 98 seconds      | $0.016      | $0.016
50 GB        | 7.5 minutes     | $0.075      | $0.075

Configuration:

# serverless.yml
functions:
  duckdbProcessor:
    handler: lambda_duckdb_processor.lambda_handler
    runtime: python3.11
    memorySize: 10240  # 10GB RAM
    timeout: 900       # 15 minutes
    ephemeralStorageSize: 10240  # 10GB temp storage
    environment:
      AWS_DEFAULT_REGION: us-east-1
    events:
      - s3:
          bucket: raw-data-bucket
          event: s3:ObjectCreated:*
          rules:
            - prefix: incoming/
            - suffix: .parquet

⚡ Option 2: Polars + Lambda (Transform-Heavy)

When to Use:

✅ Complex multi-step transformations
✅ DataFrame operations (joins, pivots, window functions)
✅ Need maximum performance for heavy compute
✅ Working with mixed data formats
❌ Not ideal for simple SQL-only queries

Implementation:

# lambda_polars_processor.py
import polars as pl
import boto3
from io import BytesIO

def lambda_handler(event, context):
    s3 = boto3.client('s3')
    bucket = event['Records'][0]['s3']['bucket']['name']
    key = event['Records'][0]['s3']['object']['key']

    # Read directly from S3 using Polars
    obj = s3.get_object(Bucket=bucket, Key=key)
    df = pl.read_parquet(BytesIO(obj['Body'].read()))

    # Complex transformation pipeline
    result = (
        df
        .filter(pl.col('revenue') > 0)
        .with_columns([
            pl.col('timestamp').dt.truncate('1d').alias('date'),
            (pl.col('revenue') * 0.9).alias('net_revenue')  # 10% commission
        ])
        .group_by(['date', 'user_id'])
        .agg([
            pl.sum('net_revenue').alias('total_revenue'),
            pl.count().alias('transaction_count'),
            pl.mean('revenue').alias('avg_revenue')
        ])
        .sort('date', descending=True)
    )

    # Write back to S3 as Parquet
    output_buffer = BytesIO()
    result.write_parquet(output_buffer, compression='zstd')

    output_key = f"processed/{key}"
    s3.put_object(
        Bucket=bucket,
        Key=output_key,
        Body=output_buffer.getvalue()
    )

    return {
        'statusCode': 200,
        'body': f'Processed {len(result)} records'
    }

Performance Characteristics:

File Size    | Processing Time | Lambda Cost | Total Cost
-------------|-----------------|-------------|------------
1 GB         | 8 seconds       | $0.0013     | $0.0013
10 GB        | 65 seconds      | $0.011      | $0.011
50 GB        | 5.8 minutes     | $0.058      | $0.058

📊 Head-to-Head Comparison

Feature	DuckDB + Lambda	Polars + Lambda
SQL Support	Native, full SQL	Expressions (SQL-like)
Performance	Excellent for analytics	20-30% faster for transforms
Memory Efficiency	Superior (columnar)	Very good
Learning Curve	Easy (SQL)	Moderate (API)
S3 Integration	Native, zero-copy	Requires download
Complex Joins	Excellent	Excellent
Best For	Analytics queries	ETL pipelines
Community	Growing	Rapidly growing

🚀 Real-World Cost Example

Scenario: Processing 5TB of event data monthly and loading into Snowflake

Traditional Approach:

EMR Cluster (m5.xlarge × 3): $1,200/month
Fivetran/Airbyte: $2,000/month
S3 Storage: $115/month
Data Transfer: $450/month
Total Pipeline Cost: $3,765/month

Snowflake (separate): ~$1,500/month
Grand Total: $5,265/month

DuckDB Lambda Approach:

Lambda Compute: $95/month
S3 Storage: $115/month
S3 Requests: $25/month
Snowpipe (included): $0/month
Total Pipeline Cost: $235/month

Snowflake (same): ~$1,500/month
Grand Total: $1,735/month

Savings: $3,530/month (67% total, 94% on pipeline)

🛠️ Deployment Steps

1. Install Dependencies:

# For DuckDB
pip install duckdb boto3 -t ./package
cd package && zip -r ../lambda_duckdb.zip . && cd ..
zip -g lambda_duckdb.zip lambda_duckdb_processor.py

# For Polars
pip install polars boto3 -t ./package
cd package && zip -r ../lambda_polars.zip . && cd ..
zip -g lambda_polars.zip lambda_polars_processor.py

2. Create Lambda Layer (Recommended):

# layer/python/requirements.txt
duckdb==0.10.0
# OR
polars==0.20.3

3. Set Up S3 Trigger:

# terraform/main.tf
resource "aws_s3_bucket_notification" "data_pipeline" {
  bucket = aws_s3_bucket.raw_data.id

  lambda_function {
    lambda_function_arn = aws_lambda_function.processor.arn
    events              = ["s3:ObjectCreated:*"]
    filter_prefix       = "incoming/"
    filter_suffix       = ".parquet"
  }
}

4. Configure Snowflake Integration:

-- Create Snowflake external stage
CREATE OR REPLACE STAGE s3_processed_stage
  URL = 's3://your-bucket/processed/'
  CREDENTIALS = (AWS_KEY_ID = 'xxx' AWS_SECRET_KEY = 'xxx');

-- Create target table
CREATE OR REPLACE TABLE user_revenue (
  date DATE,
  user_id VARCHAR,
  total_revenue DECIMAL(18,2),
  transaction_count INTEGER
);

-- Set up Snowpipe for automatic ingestion
CREATE OR REPLACE PIPE user_revenue_pipe
  AUTO_INGEST = TRUE
  AS
  COPY INTO user_revenue
  FROM @s3_processed_stage
  FILE_FORMAT = (TYPE = PARQUET)
  MATCH_BY_COLUMN_NAME = CASE_INSENSITIVE;

5. Enable S3 Event Notifications for Snowpipe:

# Add SNS notification for Snowpipe
resource "aws_s3_bucket_notification" "snowpipe_notification" {
  bucket = aws_s3_bucket.processed_data.id

  topic {
    topic_arn = "arn:aws:sns:us-east-1:xxx:snowpipe-topic"
    events    = ["s3:ObjectCreated:*"]
    filter_prefix = "processed/"
  }
}

📈 Performance Optimization Tips

For DuckDB:

# Enable parallel processing
con.execute("SET threads=8;")

# Use memory limits
con.execute("SET memory_limit='8GB';")

# Optimize Parquet reading
con.execute("SET enable_object_cache=true;")

For Polars:

# Lazy evaluation for large datasets
result = (
    pl.scan_parquet('s3://bucket/data/*.parquet')
    .filter(pl.col('date') > '2024-01-01')
    .group_by('user_id')
    .agg(pl.sum('revenue'))
    .collect(streaming=True)  # Process in batches
)

🎯 Key Takeaways

Start with DuckDB if your pipeline is analytics-heavy and SQL-based
Choose Polars if you need complex transformations and maximum performance
Always use Parquet for storage—it's 10x cheaper to query than JSON/CSV
Lambda's 15-minute limit is sufficient for 95% of data processing tasks
Monitor cold starts: Keep functions warm with scheduled pings if needed

🔍 When NOT to Use This Architecture

❌ Processing > 100GB per invocation (Lambda limits)
❌ Real-time streaming (use Kinesis + Flink instead)
❌ Complex ML training (use SageMaker)
❌ Transactions requiring ACID guarantees (use RDS)

💡 Production Checklist

[ ] Enable S3 versioning for data recovery
[ ] Set up CloudWatch alarms for Lambda errors
[ ] Implement DLQ (Dead Letter Queue) for failed invocations
[ ] Add Lambda Insights for detailed monitoring
[ ] Configure S3 lifecycle policies to archive old data
[ ] Use AWS Secrets Manager for Snowflake credentials
[ ] Set up Snowpipe with AUTO_INGEST for automatic loading
[ ] Monitor Snowpipe with SYSTEM$PIPE_STATUS()
[ ] Configure Snowflake resource monitors to control costs
[ ] Enable VPC for private data access (if required)
[ ] Test Snowflake data quality with dbt tests

🔗 Resources

Questions? Drop them in the comments—I'd love to hear about your data pipeline challenges!

What's your data processing stack? Are you using Snowflake? Let me know if you'd like a deep dive into Snowpipe optimization or any specific part of this architecture! 👇

This content originally appeared on DEV Community and was authored by vinicius fagundes

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🏗️ Architecture Overview

💰 Cost Comparison: Why This Matters

🔧 Option 1: DuckDB + Lambda (Analytics-First)

When to Use:

Implementation:

Performance Characteristics:

Configuration:

⚡ Option 2: Polars + Lambda (Transform-Heavy)

When to Use:

Implementation:

Performance Characteristics:

📊 Head-to-Head Comparison

🚀 Real-World Cost Example

Traditional Approach:

DuckDB Lambda Approach:

🛠️ Deployment Steps

1. Install Dependencies:

2. Create Lambda Layer (Recommended):

3. Set Up S3 Trigger:

4. Configure Snowflake Integration:

5. Enable S3 Event Notifications for Snowpipe:

📈 Performance Optimization Tips

For DuckDB:

For Polars:

🎯 Key Takeaways

🔍 When NOT to Use This Architecture

💡 Production Checklist

🔗 Resources

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