Snowflake Data Optimization for Faster and Smarter Travel Analytics
22s → 4s
pipeline runtime (81.82% faster)
100%
POC success across all capabilities
1,000+
lines modularized
8‑week
MVP rollout
About the Enterprise
Snowflake Data Optimization is essential for travel and hospitality companies that depend on real-time data. Booking systems, pricing engines, loyalty programs, and guest services all need fast and reliable information to deliver a great customer experience.
A leading travel and hospitality company was facing several data challenges. It relied on complex Snowflake data pipelines to manage reservations, pricing, occupancy, customer loyalty, and business reporting. As data volumes increased, the existing system became slower and more expensive to run.
Many data pipelines depended on large stored procedures that were difficult to manage and update. Metadata was spread across different systems, making it hard for teams to find trusted data. Data refresh cycles also became slower, which delayed business reports and affected decision-making.
The company wanted to improve performance without increasing cloud costs. It also wanted a platform that could automate data management, improve governance, and support future AI initiatives.
To achieve these goals, the organization invested in Snowflake Data Optimization to modernize its data platform and simplify daily operations.
Challenges in Snowflake Data Optimization
As the business grew, several challenges became clear.
- Slow data pipelines delayed reports.
- Compute costs increased every month.
- Large stored procedures were difficult to maintain.
- Metadata was spread across different systems.
- Data quality checks required manual effort.
- Business users waited longer for updated information.
- Developers spent too much time fixing pipeline issues.
- Scaling new workloads became more expensive.
These challenges affected pricing decisions, booking systems, operational planning, and customer experience.
The organization needed a faster and more efficient data platform that could reduce costs while improving performance.
Modernizing Snowflake Data Optimization
The company implemented the Inferyx Data Intelligence Platform to improve Snowflake Data Optimization across the enterprise. Instead of using one large stored procedure, the platform divided more than 1,000 lines of code into smaller and easier-to-manage workflows.
This new approach made data pipelines simpler, faster, and more reliable. One pipeline that previously took 22 seconds completed in 4 seconds. Other workloads improved from 11 seconds to 1 second, 5.5 seconds to 2 seconds, and 2.7 seconds to 1 second.
These improvements reduced compute usage while delivering faster data processing. The platform also automated many routine tasks, allowing developers to spend more time on innovation instead of maintenance.
Faster Pipelines with Snowflake Data Optimization
The new Snowflake Data Optimization solution introduced automated pipeline orchestration across the enterprise.
Data moved faster between booking systems, pricing engines, operational dashboards, and reporting platforms. Teams no longer had to wait for long refresh cycles before making business decisions.
The platform also included automated deployment using CI/CD pipelines. As a result, updates became faster and more reliable while reducing deployment errors. The new architecture improved system stability and made future enhancements easier.
Better Data Quality and Governance
Reliable data is important for every travel company. The platform introduced automated data quality checks that continuously monitored incoming data. Scorecards helped teams quickly identify missing or incorrect information before it affected business reports.
A governed data catalog made it easier for employees to find trusted datasets. Complete metadata lineage also showed where data came from, how it moved through the system, and how it was used.
These capabilities improved transparency and supported stronger governance across the organization. Because data became easier to trust, business users spent less time validating reports and more time making decisions.
Lower Costs and Greater Flexibility
One of the biggest benefits of Snowflake Data Optimization was lower cloud spending. The platform reduced dependence on Snowflake compute by supporting Apache Spark and Apache Iceberg for selected workloads.
This gave the organization greater flexibility when processing large datasets. Instead of using expensive compute resources for every workload, teams could choose the most cost-effective processing option.
This approach improved performance while controlling cloud expenses. The organization also achieved 100% success across all proof-of-concept requirements, confirming that the new platform was ready for enterprise-scale deployment.
Business Impact and Outcomes
The modernization project delivered measurable business value. Compute time decreased by up to 81.82%, allowing data pipelines to finish much faster.
Overall infrastructure costs dropped by 63–72%, helping the company control cloud spending while supporting business growth. Reservation, pricing, availability, and operational datasets refreshed automatically without manual intervention.
Because information became available much sooner, teams responded faster to changing customer demand. Customer-facing analytics also improved.
Marketing teams delivered more personalized offers. Revenue teams improved pricing decisions. Operations teams forecast demand with greater accuracy. Leadership gained faster access to trusted business insights.
The platform also strengthened governance through automated quality checks, metadata management, and continuous compliance monitoring. Today, the company operates a modern Snowflake Data Optimization platform that delivers fast data processing, lower costs, stronger governance, and better business performance.
The organization now has an AI-ready data foundation that supports future innovation while improving the experience for both employees and travelers.