Storage Optimization: Optimize Data Lifecycle Policies

Storage Optimization: Optimize Data Lifecycle Policies

Optimize data storage strategies, implement data lifecycle policies, and reduce unnecessary data retention to minimize storage costs.

The Storage Cost Problem

Storage costs accumulate over time. Without proper lifecycle policies, you're paying to store data that may no longer provide business value.

Data Lifecycle Strategy

1. Hot Data: Frequently accessed, kept in Snowflake tables
2. Warm Data: Occasionally accessed, can be archived to external stage
3. Cold Data: Rarely accessed, moved to cheaper external storage
4. Deleted Data: No business value, can be permanently removed

Implementing Archive Strategy

-- Archive old data to S3/Azure/GCS
COPY INTO @archive_stage/2023_data/
FROM (
SELECT * FROM production_table
WHERE date_column < '2024-01-01'
)
FILE_FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY);

-- Delete from production after verification
DELETE FROM production_table
WHERE date_column < '2024-01-01';

Creating External Tables for Archived Data

-- Query archived data when needed without loading
CREATE EXTERNAL TABLE archived_orders (
order_id INTEGER,
order_date DATE,
amount NUMBER
)
LOCATION = @archive_stage/archived_orders/
FILE_FORMAT = (TYPE = PARQUET);

Best Practices

• Define retention policies based on business requirements
• Archive data older than active use period
• Use external tables for occasional access to archived data
• Automate archival process with scheduled tasks
• Document what data is archived and where

Real-World Example

A company kept 5 years of transaction history in active tables (50TB storage, $12,000/month). After implementing lifecycle policy: Hot data (last 12 months) in Snowflake: 12TB = $2,880/month, warm data (1-3 years) in external stage: $500/month, cold data (3-5 years) in S3 Glacier: $100/month. Total: $3,480/month. Savings: $8,520/month (71% reduction).

Key Takeaways

• Implement data lifecycle policies based on access patterns
• Archive infrequently accessed data to external storage
• Use external tables to query archived data when needed
• Automate archival process
• Can reduce storage costs by 50-80%