Step Snap 1 [Cost Reduction Best Practices]

1. Query Cost Planning

"Think of BigQuery costs like a taxi meter - it charges based on how much data you scan. Let's explore how to keep that meter running efficiently."

-- Price Estimation Best Practice
#1: Always estimate query cost before running
SELECT COUNT(DISTINCT user_id)
FROM `dataset.large_table`
WHERE date >= '2024-01-01'
# Use --dry-run flag to estimate cost

Deep Dive:

• BigQuery charges based on data processed, not query result size
• The pricing model: $5/TB (may vary by region)
• Cached queries are free but cache expires after 24 hours
• Columns in SELECT impact cost directly, even if filtered out later
• Partitioned tables can reduce cost by up to 90% in time-based queries

2. Materialization Strategy

"Imagine building a complex LEGO structure. Instead of trying to build everything at once, you create stable intermediate pieces."

-- Bad Practice: Single Complex Query
SELECT complex_metrics
FROM (SELECT * FROM huge_join)
WHERE complex_conditions;

-- Good Practice: Staged Materialization
CREATE TEMP TABLE stage1 AS (
    SELECT * FROM base_table
    WHERE date = '2024-02-09'
);
CREATE TEMP TABLE stage2 AS (
    SELECT * FROM stage1
    JOIN lookup_table
);

Under The Hood:

• Each materialization stage creates a checkpoint for your data
• Benefits:
  • Easier debugging: You can inspect intermediate results
  • Cost control: If something fails, you don't pay for rerunning everything
  • Resource optimization: BigQuery can better optimize each stage
  • Query development: Easier to develop and test complex logic
• Common use cases:
  • ETL pipelines
  • Complex analytical queries
  • Machine learning feature preparation
  • Reporting workflows