Cost Optimization Best Practices¶
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Best practices for optimizing costs in Cloud Scale Analytics.
Overview¶
Cost optimization is achieved through:
- Right-sizing resources
- Leveraging auto-scaling and auto-pause
- Using reserved capacity for predictable workloads
- Implementing efficient data architectures
For detailed guidance, see: Full Cost Optimization Guide
Quick Wins¶
1. Auto-Pause Dedicated SQL Pools¶
# Configure auto-pause (15 minutes inactivity)
az synapse sql pool update \
--name dedicated-pool \
--workspace-name synapse-ws \
--resource-group rg-analytics \
--enable-auto-pause true \
--auto-pause-delay 15
2. Use Serverless SQL for Ad-Hoc Queries¶
-- Query external data without dedicated resources
SELECT
CustomerSegment,
COUNT(*) AS CustomerCount,
AVG(TotalPurchases) AS AvgPurchases
FROM OPENROWSET(
BULK 'https://storage.dfs.core.windows.net/data/customers/*.parquet',
FORMAT = 'PARQUET'
) AS customers
GROUP BY CustomerSegment;
3. Implement Tiered Storage¶
| Tier | Use Case | Cost |
|---|---|---|
| Hot | Active data (< 30 days) | $$$$ |
| Cool | Infrequent access (30-90 days) | $$$ |
| Archive | Compliance/historical (> 90 days) | $ |
4. Schedule Spark Clusters¶
# Auto-terminate idle clusters
cluster_config = {
"autotermination_minutes": 30,
"enable_autoscaling": True,
"min_workers": 1,
"max_workers": 10
}
Cost Monitoring¶
Budget Alerts¶
az consumption budget create \
--budget-name monthly-analytics \
--amount 10000 \
--time-grain Monthly \
--start-date 2025-01-01 \
--end-date 2025-12-31 \
--notifications '{
"actual_vs_budget_80_percent": {
"enabled": true,
"operator": "GreaterThan",
"threshold": 80,
"contactEmails": ["team@company.com"]
}
}'
Cost Analysis Query¶
// Monthly cost breakdown by service
AzureDiagnostics
| where TimeGenerated > ago(30d)
| summarize
EstimatedCost = sum(toreal(Quantity) * toreal(UnitPrice))
by ResourceType, bin(TimeGenerated, 1d)
| render timechart
Related Documentation¶
- Resource Planning
- Auto-Scaling Configuration
Last Updated: January 2025