integration_ARCHITECTURE
Structural AI Intelligence for BigQuery
Harness the massive, petabyte-scale power of Google BigQuery. Our AI natively unwraps nested arrays and enforces strict partition scanning guardrails to keep your GCP costs low.
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// STRATEGIC_SCENARIO
Deep Data Retrieval
How Arcli grounds AI in your exact schema to generate highly-optimized, dialect-specific execution logic.
Automated GA4 Telemetry Extraction
Advanced data extraction pattern.
THE EXECUTIVE FILTER (ROI)
Unlocks the true value of raw GA4 data without wrestling with the rigid GA4 UI or waiting days for engineers to build complex flattening pipelines.
- Fully optimized for SQL constraints.
- Bypasses semantic layer hallucinations via strict schema grounding.
SQL_COMPILE
SELECT
device.category AS device_category,
SUM((SELECT value.double_value FROM UNNEST(event_params) WHERE key = 'value')) AS total_revenue
FROM `gcp-project.analytics_12345.events_*`
WHERE event_name = 'purchase'
-- AI automatically injects strict partition boundaries to save slot costs
AND _TABLE_SUFFIX BETWEEN FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY))
AND FORMAT_DATE('%Y%m%d', CURRENT_DATE())
GROUP BY 1
ORDER BY total_revenue DESC;// DOCUMENTATION
Expert Insights
Everything you need to know about implementing Arcli's engine into your stack.
How does Arcli prevent full-table scans in BigQuery?
Our engine is partition-aware. It mandates date filters or `_PARTITIONTIME` boundary conditions in the generated SQL before it is sent to GCP, protecting your slot budget.
Can it handle complex nested arrays in GA4 and Firebase?
Absolutely. The engine is explicitly trained on Google Standard SQL and understands how to generate the complex `UNNEST()` functions for REPEATED fields dynamically.
Is my raw data used to train external AI models?
No. Raw BigQuery row data never touches our LLM infrastructure. We only vectorize the table schemas (metadata) to understand relationships and author the SQL.
Related Resources
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