Alteryx One Live Query and Google Cloud BigQuery redefine how enterprises handle complex, unstructured data at scale by reducing reliance on disconnected tools, minimizing data movement, and enabling warehouse-native execution.

Live Query provides an intuitive, browser-based environment that allows business users to build sophisticated data pipelines without writing a single line of code. When paired with the scale and performance of BigQuery, this duo enables secure SQL pushdown, meaning data transformation logic is executed directly within the warehouse rather than moving it across the network. This synergy is particularly potent for document-heavy workflows, where Live Query can orchestrate Google’s advanced AI and machine learning models — like Gemini — to extract intelligence from PDFs and images while maintaining the strict data governance and security of the BigQuery environment.

Finance and operations teams often find themselves buried under a mountain of vendor invoices. These documents come as PDFs with inconsistent data, varying invoice number formats, and a high risk of manual error. Traditionally, data teams have had to resort to manual data extraction or use a fragmented series of tools to move data into a warehouse — a process that is both time-consuming and prone to duplicates or amount mismatches.

With the integration of Live Query for BigQuery , you can now automate the entire lifecycle of an invoice. By processing PDFs, extracting key fields, and standardizing results directly within the BigQuery ecosystem, data teams can identify exceptions like anomalies or data issues within the documents faster while ensuring their governed enterprise data configured through the BigQuery fine grained access policies never has to leave the warehouse.

Let’s dive deeper into how the Live Query solution works. Invoice PDFs arrive as unstructured documents in Google Cloud Storage. In the Live Query workflow, the Document Extract tool extracts structured fields using the model selected by the user, such as Gemini or Document AI. The results are then standardized for reliable matching and compared against historical invoice records already stored and governed in BigQuery.

This automated workflow is designed to move from document to decision with minimal manual intervention. At enterprise scale, that pattern is not just about processing a handful of PDFs — it is about enabling analytics and AI over millions of unstructured documents while keeping reconciliation logic aligned with BigQuery.

By combining AI-driven extraction with BigQuery-native execution, the solution provides:

Higher straight-through processing : Reducing manual handling and allowing more invoices to move through the process automatically.