Data lake – Revolutionizing Travel Data Management

Embark on a journey through INTechHouse's revolutionary solution, reshaping travel data management on a global scale.

About client

Our Dutch client, a prominent player in the global online travel sector, distinguishes itself by offering multi-stage flight connections, often including smaller airlines. Their market prominence spans over 50 countries, collaborating with various partners for comprehensive booking services.

Business challenge

Amidst dynamic growth and acquisitions, the challenge was to integrate diverse systems effectively, manage escalating data volumes, and optimize processes to offer tailor-made solutions.

Team composition

Our versatile team, including a Solution Architect, Project Manager, Developers, DevOps, and Data Engineer, synergized efforts to address the client’s evolving needs.

Our solution

The focal point was the implementation of a solution, consisting of key phases:

  • Data Gathering:
    Aggregating vast data from diverse sources into a centralized system, accommodating structured, semi-structured, and unstructured data.
  • Cleaning and Aggregation:
    Streamlining data by eliminating errors and irrelevant information, preparing it for meaningful analysis.
  • Relationship Identification:
    Analyzing data for dependencies and patterns crucial for enhanced decision-making.
  • Reporting:
    Developing reports and business analyses derived from processed data.

CDC Mechanism:

Change Data Capture (CDC) was integrated into data gathering, enabling real-time data change collection, enhancing the online processing of information, rather than the reporting stage.

Technology used in project

Kafka, PHP, PostgreSQL, Debezium, Cockroach DB, Java Spring Boot, Talend, ETL

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Value we added

  • Integrated Data Management: Efficiently integrating and managing large volumes of data from various systems.
  • Streamlined Decision-Making Processes: Faster data access and improved quality for more informed business decisions.
  • Scalability and Flexibility: System scalability aligned with the company’s growth needs without compromising efficiency.
  • Cost Optimization: Operational cost reduction through centralized and automated data collection and processing.
  • Market Competitiveness: Enhanced offerings by better understanding customer preferences and responding rapidly to market changes.

Future perspective

  • Analytics Expansion: Utilizing gathered data for advanced predictive and prescriptive analyses.
  • Integration with New Technologies: Incorporating modern technologies like AI and ML for deeper data analysis.
  • International Expansion: Leveraging data for international market strategies and identifying growth opportunities.
  • Customer Offer Personalization: Enhancing personalized offerings based on a deeper understanding of customer needs.
  • Increased Operational Efficiency: Continuously optimizing internal operations for faster data processing and analysis.