Sample Analysis

Pipeline: dbt_cloud_agent_test Run ID: manual__2025-09-12T19:30:18.065535+00:00 Analysis Date: September 17, 2025

Executive Summary

• Total Pipeline Cost: $0.0023 USD across 66 queries executed between 19:30:18 - 19:30:45 UTC • Cost Breakdown: Marts stage ($0.0009) was the most expensive, followed by Testing phase • Performance: Average query duration of 0.79 seconds with 636KB total data processed

Detailed Cost Breakdown

Pipeline Stage Analysis

Stage	Query Count	Cost (USD)	Data Processed	Avg Duration
Marts	9 queries	$0.0009	153KB	3.5 seconds
Staging	15 queries	$0.0000	58KB	0.2 seconds
Testing	28 queries	$0.0000	425KB	0.4 seconds
Other	14 queries	$0.0014	-	-

Top Cost-Contributing Models

1. mart_partner_channel_analysis - $0.0002 (76KB processed)

2. mart_new_sales_performance_dashboard - $0.0002 (19KB processed)

3. mart_sales_perfromance_dashboard - $0.0002 (19KB processed)

4. Data quality tests - Various micro-costs across 28 test queries

Trend Analysis

Historical Context: This pipeline run represents a typical execution pattern. The cost profile shows:

• Staging models: Efficient view-based transformations with minimal cost

• Mart models: Higher cost due to table materialization and complex joins

• Testing layer: Comprehensive but cost-effective data quality validation

Optimization Recommendations

High Priority

1. Review mart model queries: 3 mart models account for 39% of total cost

2. Optimize data quality tests: 28 test queries could be consolidated

Medium Priority

1. Consider incremental models: For frequently updated marts to reduce processing

2. Implement data partitioning: For larger datasets in future iterations

Low Priority

1. Monitor staging efficiency: Current performance is optimal

2. Review test coverage: Ensure tests provide value relative to execution frequency

Technical Validation Details

Data Sources Utilized:

• Job execution logs and cost data: Primary source for cost attribution

• Analytics Infrastructure: 66 records stored for historical analysis

• Model metadata: Compilation and dependency context

Analysis Methodology:

• Time Window: ±30 minute correlation window for job matching

• Cost Attribution: Extracted model information from query execution data

• Stage Classification: Pattern matching on model names (stg_, int_, mart_ prefixes)

• Quality Validation: Cross-referenced multiple data sources for accuracy

Dashboard Updates

Created Dashboard: "Pipeline Cost Analysis Dashboard"

Dashboard ID: 12

Dashboard Components:

1. Historical Cost Trends - Line chart tracking pipeline costs over time with 7-day rolling averages

2. Stage Cost Breakdown - Stacked column chart showing cost distribution by pipeline stage

3. Top Expensive Queries - Table with optimization recommendations and performance metrics

4. Pipeline Summary Metrics - Key statistics with run-to-run comparisons

Key Dashboard Features:

• Filter Controls: Pipeline name, run ID, and date range filtering

• Trend Analysis: Week-over-week and month-over-month cost comparisons

• Performance Insights: Query efficiency metrics and optimization flags

• Cost Attribution: Detailed breakdown by models and pipeline stages