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Using Your BigQuery Connector

Once your BigQuery connector is set up:
  1. Start a conversation with Julius
  2. Ask about your data using natural language:
    • “Show me sales data from the last quarter”
    • “What’s the average order value by region?”
    • “Create a chart showing user growth over time”
  3. Julius will automatically:
    • Connect to your BigQuery project
    • Write and execute SQL queries
    • Handle BigQuery’s specific syntax and functions
    • Present results in easy-to-understand formats
    • Create visualizations when requested
Julius understands BigQuery’s unique features like nested/repeated fields, array functions, and standard SQL syntax. You don’t need to know BigQuery-specific SQL!

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Query optimization for large-scale BigQuery warehouses

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Basic mechanics - How BigQuery pricing works

  1. BigQuery charges per TB of data scanned (not stored)
  2. When you run SELECT * on a 100M record table, you pay for scanning every column
  3. Unfiltered queries scan entire tables regardless of result size
  4. Partitioned tables allow BigQuery to skip irrelevant data chunks
  5. Clustered tables organize data for faster retrieval within partitions

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Key characteristics of cost-effective queries

  • Column selectivity: Only request needed columns (SELECT specific_cols vs SELECT *)
  • Row filtering: Use WHERE clauses with partitioned/clustered columns
  • Partition pruning: Filter on partition keys (usually date/timestamp)
  • Early aggregation: Group/summarize before joining large tables
  • Query caching: Identical queries reuse previous results

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Concrete examples

Expensive query (might scan 3TB)
SELECT * FROM sales_data 
WHERE customer_id = '12345'
Optimized query (might scan 50GB)
SELECT customer_id, order_date, total_amount 
FROM sales_data 
WHERE DATE(order_date) >= '2024-01-01' 
  AND customer_id = '12345'
Reach out to team@julius.ai for support or to ask questions not answered in our documentation.