Data mesh breaks away from the traditional centralized data lake model in favor of a decentralized approach. In this model, each business domain—such as bookings, customer service, and marketing—owns and manages its data as a product. This shift not only enhances data quality and accessibility but also accelerates innovation across the organization.
The cloud remains essential to data mesh, offering scalable storage, compute power, and enterprise-grade security. But instead of housing a single, monolithic data repository, the cloud supports a distributed network of domain-specific data products. This allows each domain in a travel business to manage its data autonomously while still benefiting from the performance and scalability of the cloud.
Let’s take a step back. In the early days of digital transformation, travel companies turned to traditional data lakes to consolidate vast datasets. Centralizing data from bookings, customer service, payments, and third-party aggregators was meant to simplify reporting and unlock analytical capabilities.
At first, it worked. But as the volume, variety, and velocity of data grew, the centralized model started to break down.
Rather than empowering teams, the central data lake became a bottleneck. Marketing, operations, and customer experience teams were often left waiting for access. Data pipelines failed without warning. Context was lost across teams. And the model couldn’t keep up with the industry’s demand for speed and autonomy.
Data mesh architecture transforms each business domain into its own data product team—responsible for producing, maintaining, and delivering high-quality data products.
Here’s how the shift works:
Each team owns its data. For example, the bookings team manages reservation data, loyalty handles engagement metrics, and marketing controls campaign data.
Data is curated, versioned, and documented—continuously improved based on feedback from data consumers like analysts and ML engineers.
Teams can independently discover, access, and consume data without relying on a central engineering team, accelerating experimentation and innovation.
Security, compliance, and quality are maintained through cross-domain policies, ensuring local autonomy doesn’t lead to chaos.
This isn’t just a technical upgrade—it’s a cultural shift. In practice, it means customer experience teams can trigger real-time personalization campaigns without waiting for monthly data dumps. Operations teams can predict rebooking trends instantly. Data becomes a strategic asset—accessible to the people who need it, when they need it.
Imagine the marketing team doesn’t have to submit a ticket to get segmentation data. Or the loyalty team can experiment directly with reward models. In a decentralized model, each team owns its data pipeline and product—freeing up time, reducing dependencies, and boosting innovation.
This unlocks:
So, how do you put this into practice? With the right tools that empower decentralization and autonomy—without sacrificing control or security.
Snowflake is more than a cloud data warehouse—it's a platform designed for collaboration at scale. For global travel companies, it acts as a centralized foundation that supports decentralized, domain-owned data architectures.
Elastic Scaling: Each domain—e.g., marketing or loyalty—can scale compute and storage independently based on their needs.
Secure Sharing: Snowflake’s zero-copy data sharing enables seamless data exchange between domains without duplication.
Governance at Scale: Role-based access, policies, and fine-grained permissions ensure compliance, even in highly regulated regions like the EU—with regional availability such as Snowflake Amsterdam.
Security, compliance, and quality are maintained through cross-domain policies, ensuring local autonomy doesn’t lead to chaos.
While Snowflake focuses on structure and access, Databricks specializes in advanced computation—ideal for teams pushing boundaries with machine learning, real-time data pipelines, and large-scale transformations.
Here’s what makes it ideal for a data mesh:
Data engineering workflows can be fully owned by domain teams. The bookings team can manage its own data lake pipelines; the marketing team can run its own campaign analytics.
Unified batch and streaming means data scientists can build models on fresh, real-time datasets—crucial in travel where context changes quickly.
ML and AI workflows are embedded, making it easier to experiment and deploy domain-specific models without relying on central data science teams.
By combining the scalable data-sharing power of Snowflake with the analytical firepower of Databricks, travel organizations can finally realize the vision of a truly decentralized data mesh.
Example:
A global airline’s pricing team uses Databricks to build a dynamic pricing engine. It pulls real-time inputs from Snowflake—fed by bookings and loyalty teams. The results are written back into Snowflake and distributed to regional sales teams, triggering promotions directly in user-facing apps.
