Moving data remains one of the biggest invisible tolls of modern analytics in 2026. Every export to an external tool involves costs, uncontrolled copies, friction between teams, and above all, increased risk surface. In this context, Alteryx has announced an expansion of its collaboration with Google Cloud with a clear message: if the data resides in BigQuery, the business logic and preparation should be executed there, without downloads or parallel “extracts”.
The centerpiece of the announcement is Live Query for BigQuery, now generally available (GA) within Alteryx One. The proposal: enable business users and technical teams to build workflows with Alteryx and run them directly in BigQuery, maintaining security and governance within the Google Cloud environment. Simultaneously, Alteryx is preparing Alteryx One: Google Edition, a “Google-first” edition designed for organizations standardizing their data stack on Google Cloud, which will be marketed via Google Cloud Marketplace.
The gap they’re aiming to close: where the data is and where decisions are “cooked”
Many companies have centralized their data in a cloud data warehouse, but preparation and modeling are still done outside: spreadsheets, uncontrolled scripts, duplicated pipelines, or “craftsman” processes only understood by their creators. The known consequence: mismatched metrics across departments, complex audits, and a constant bottleneck in data teams.
Alteryx suggests that this gap worsens with the advent of AI in critical processes (revenue, risk, compliance, operational planning), where it’s not enough to be accurate: you must be able to explain the “how” and the “why” of every result. Their argument is that models do not “guess” business definitions or data quality; they require repeatable, governed, and auditable rules.
Live Query for BigQuery: practical changes
The promise of Live Query is straightforward: workflows are designed in Alteryx but executed in BigQuery. This removes the intermediate step of extracting data from the warehouse for processing elsewhere, a move that typically introduces two problems: cost (including egress charges when applicable) and exposure.
Functionally, the announcement targets two very different audiences:
For business teams (“information workers”)
- More direct access to large datasets in BigQuery via a no-code experience.
- Transformations, rules, and calculations without writing SQL, avoiding downloads, and without turning work into a chain of local files.
- Rapid iteration: adjusting logic and re-running without rebuilding pipelines in parallel.
For IT and data teams
- Fewer “shadow pipelines” and uncontrolled extracts: data stays where it is.
- Simplified governance and security: execution within the Google Cloud perimeter.
- Enhanced traceability and auditability of applied logic (especially important when analytics impact financial reporting, compliance, or risk management).
TechTarget also highlights a practical effect many organizations pursue: reducing the risk of leaks associated with data transfer between platforms, and avoiding costs related to moving large volumes outside the warehouse.
Alteryx One: Google Edition, a move toward “Google-first”
The second announcement has a more packaging and adoption focus. Alteryx One: Google Edition is described as a simplified, optimized edition for the Google ecosystem, with native integration with BigQuery, Google Sheets, and Google Drive, and availability via Marketplace to facilitate purchasing, deployment, and standardization.
In practice, these “cloud-first” editions usually respond to a simple reality: many companies have already chosen their platform (in this case, Google Cloud) and want the rest of the stack to reduce friction, align with their data governance, and avoid multiplying tools or alternative routes.
Strategic perspective: preparing data for AI without losing control
There’s a recurring underpinning in the narratives of major data providers: AI is raising the bar. Where dispersion was once tolerated (a calculation in Excel here, a script there), the focus now is on consistency, reuse, and controlling data lineage. Not out of pure technical purism, but because automated results impact real decisions and create responsibility.
In this vein, Alteryx emphasizes turning business logic into a governed, reusable asset rather than something “buried” in spreadsheets or isolated code. Google Cloud frames this movement as a way for more teams to leverage BigQuery in applied analytics, accelerating from data to action.
Quick overview: what each component adds
| Component | Where it happens | What it enables | Main benefit |
|---|---|---|---|
| Live Query for BigQuery | Within BigQuery (on Google Cloud) | Alteryx workflows executed “in-place” | Less data movement, more control |
| Alteryx One (workflows) | Design in Alteryx | Preparation, rules, automation | Standardize business logic |
| BigQuery (warehouse + scale) | Google Cloud | Query/execution at scale | Performance and centralized governance |
| Marketplace + Google Edition | Google Cloud Marketplace | Simplified adoption and purchase | Less operational friction and procurement hassle |
Key considerations: costs and work culture
As with any “pushdown” approach (bringing computation to the warehouse), two realities typically emerge:
- The experience changes. Some users are accustomed to bringing data into their environment and working “comfortably” outside the warehouse. Live execution may require habit adjustments, especially in highly iterative workflows.
- Cost governance becomes more important. Running more processes inside the warehouse can improve security and scale, but also makes consumption impact more visible if not managed with good controls and planning.
Still, the movement aligns with market trends: fewer copies, more traceability, and more analytics where the data resides—particularly when the goal is to feed AI with prepared information and stable rules.
Frequently Asked Questions
What is Live Query for BigQuery, and what is it used for?
It is an Alteryx One feature that allows designing workflows and executing them directly within BigQuery, avoiding data movement outside the data warehouse and enhancing governance and security.
Does Live Query for BigQuery eliminate the need to know SQL?
The idea is that many preparation and calculation flows can be done with a no-code experience, though advanced environments may still benefit from knowing SQL to optimize, validate, or extend logic.
What are the advantages of “in-place” analytics in BigQuery for regulated companies?
It reduces copies and extracts, centralizes controls, and facilitates traceability of logic applied to data—key aspects for reporting, risk management, auditing, and compliance.
What is Alteryx One: Google Edition, and when is it useful?
It is a “Google-first” edition of Alteryx One designed for organizations mainly working with Google Cloud, offering tighter integration with BigQuery and available via Google Cloud Marketplace to simplify adoption.