Vio.com is trusted by 100 million users each year to find better value, every stay offering savings of up to 45%. The platform discovers exclusive accommodation deals and compares prices across all the top travel sites, so travelers can book the best option with ease. Its exceptional customer support team is available 24/7, which is reflected in its rating on Trustpilot and rapid growth.
Vio uses Snowflake to manage data and Looker for business intelligence. They rely on dbt for modeling and transformation to support 10,000+ tables, 1200+ dbt models, and 1000+ Looker dashboards, along with dbt tests and in-house CI checks during the development lifecycle to ensure data quality and codebase stability.
To power scale with high efficiency, Vio used a modern data stack, including Snowflake, dbt, and Looker. These tools democratized data access so all users could easily build tailored reports without waiting for IT support. However, this flexibility also made Vio’s data stack more sensitive to change and somewhat prone to errors.
As data analytics needs increased, Vio expanded their team, empowering more people to produce their own data assets. This strategy improved throughput — but as more team members with varying expertise modified data, keeping information accurate and up-to-date became increasingly complex.
“Even in the most modern data stack, one simple code modification can ripple through the entire ecosystem,” Engineering Manager Iñigo Hernaez says. “This affects everything from A/B testing for product features to our partnerships.”
While Vio’s team helped each other assess and implement changes, these manual reviews diverted valuable time away from improving code efficiency. Furthermore, they did not provide the end-to-end visibility necessary to identify all potential data discrepancies. Yet, as Vio welcomed new hires, the need for collaboration only increased to ensure everyone held the same standards for data quality.
To anchor company decisions in accurate, complete data, Iñigo and his team started looking for a data management tool that would help maintain consistent data governance across Vio’s data stack. They explored a few ready-made platforms and even experimented with in-house solutions — but nothing delivered the ROI and scalability needed.
That’s when Vio found Foundational.
“A data change can impact things your team may be unaware of, leading folks to draw potentially flawed conclusions about growth initiatives. We needed a tool to give us end-to-end visibility into every modification.”
Foundational seamlessly integrated with Vio’s GitHub workflow, providing automatic lineage coverage across the entire data lifecycle, at build time. The platform analyzes source code, query logs, and metadata so Iñigo and his organization can proactively track the impact of code changes on Vio’s data. This visibility helps them maintain efficiency and compliance throughout the entire dev process and fuel data-driven business initiatives.
The biggest game changer? This validation happens at build time before any code deploys. With Foundational’s Code Validation and Downstream Impact Analysis, analytics engineers, analysts, and data scientists have new code scanned pre-merge, receiving CI Checks and impact analysis as GitHub comments, inline with pull requests.
These insights enable the team to easily evaluate how proposed changes will affect downstream systems, reports, and dashboards, and validate each request. As a result, everyone can push new dbt and LookML code with confidence as they build new data products for Vio, all while minimizing the time spent on ad-hoc code change reviews.
Stakeholders can also set custom alerting rules so every upstream update is communicated before it can affect the data. These notifications keep Vio’s teams aligned on their data management approach regardless of experience level.
Foundational also continuously monitors all live code and data to identify potential data inconsistencies in case they do not originate in code changes. This capability enhances team productivity, freeing up more time for strategic quality assurance.
“Foundational is embedded into our entire dev cycle. Our teams can now make changes independently without worrying about potential data discrepancies across the rest of the ecosystem.”
Today, Iñigo and his team use Foundational to trace all downstream effects of their code, preventing any potential data issues at build time. The platform allows them to maintain high data integrity and operational efficiency with minimal lift, even as new team members with diverse experience levels come in.
With this end-to-end data visibility, everyone across the organization can easily make the right calls about the business, accelerating Vio’s growth initiatives.
Looking ahead, Vio plan to make Foundational a key element of their quarterly performance sprints. Leaning on the platform’s lineage coverage, they will continue to visualize complex data flows and dependencies, unlocking cost-saving opportunities with streamlined data processing.
“Foundational has become an indispensable tool. We now have a unified QA and testing process, allowing us to focus on refining high-level processes for more time and cost savings.”
