Introduction: Defining the Challenge
In today’s data-driven world, businesses rely on timely, actionable insights to optimize operations and drive efficiency. For B2B SaaS providers in the travel and hospitality sector, delivering seamless analytics and reporting to their customers is a critical value proposition. However, many companies face significant challenges that hinder their ability to provide high-performance data services:
- High Data Platform Costs: The expenses associated with existing data infrastructure can become unsustainable, limiting scalability.
- Data Availability Delays: Operational data for customers is often only accessible the next day, impacting decision-making and real-time insights.
These bottlenecks limit the potential to provide instant, data-driven value to both suppliers and buyers. To remain competitive, a transformation was necessary—one that optimized costs and drastically reduced data latency.
Defining Goals and Metrics
To address these challenges, we set out with two key objectives:
- Reduce data storage and computing costs by at least 50% without compromising performance or scalability.
- Reduce analytics latency for clients allowing them to have timely and accurate information of their operations.
Success would be measured through:
- Cost savings in cloud storage and compute resources.
- Reduction in time required for data ingestion and availability on the platform.
- Improved user experience and customer satisfaction due to faster insights.
Achieving these goals would mean that customers—hoteliers and travel groups—could access and act on critical data insights almost instantaneously.
Exploring Solutions and Innovations
To overhaul the data architecture, we implemented a Change Data Capture (CDC) process with an event-driven architecture, leveraging modern cloud-native technologies:
- MongoDB: Used as the operational database for storing transactional data.
- AWS EventBridge: Facilitated event-driven communication to trigger real-time data updates.
- AWS Lambda: Serverless compute for processing CDC events efficiently.
- AWS S3: Used as the primary data lake solution for staging and general storage.
- Snowflake: Enabled scalable analytics and reporting with fast query performance.
Key Implementation Steps
- Capturing Real-Time Changes: A CDC pipeline was built to detect changes in MongoDB and trigger events in AWS EventBridge.
- Processing Events Efficiently: AWS Lambda functions processed the changes dynamically and structured them for downstream analysis.
- Optimizing Storage & Compute: Leveraging AWS S3 for intermediate storage significantly reduced Snowflake compute costs.
- Accelerating Data Availability: The new architecture allowed updates to be ingested, processed, and made available in under 8 minutes.
By designing a lean, event-driven data pipeline, the company was able to minimize redundancy, optimize resource usage, and improve system responsiveness.
Assessing Impact and Insights
The results of the transformation were both immediate and substantial:
- 60% Reduction in Storage and Compute Costs: By shifting to an event-driven approach and leveraging efficient storage solutions, infrastructure costs were significantly lowered.
- 80% Improvement in Data Availability: What once took 24 hours was now accessible within 8 minutes, providing near real-time insights to users.
- Enhanced Customer Experience: Faster access to data meant that hotel buyers and suppliers could make more informed decisions instantly.
Lessons Learned & Future Outlook
- Event-driven architectures are a game-changer for businesses seeking real-time insights while keeping costs under control.
- Selecting the right cloud-native technologies ensures scalability, performance, and long-term sustainability.
- Transparency in data strategy and operations fosters trust among platform users, reinforcing brand credibility.
As the company continues to evolve, the foundation set by this transformation enables further innovation. Future enhancements may include AI-driven insights, predictive analytics, and deeper integrations with third-party travel ecosystems.
