Travel Data Analytics

TUI Group UK - Big Data Specialist

Built analytics-ready data pipelines for travel, customer, flight, and financial datasets across multiple source systems. The goal was to turn fragmented structured and semi-structured data into reliable insights for business decisions.

Role Big Data Specialist

Domain Travel Analytics

Focus Cloud Data Platform

Solution Design

Unified analytics pipeline for multi-source travel data.

Hadoop, Spark, cloud storage, relational databases, and NoSQL stores were used to build scalable ingestion, transformation, and analytics workflows.

Source Systems

Sqoop + HDFS

Hive Warehouse

Spark Processing

Python Analytics

Business Insights

Problem

TUI had many operational systems storing customer, flight, and financial data in different formats, making analytics slow and fragmented.

Structured and semi-structured data sources
Large-volume operational datasets
Need for faster decision support

Engineering

Built scalable data processing workflows using Hadoop ecosystem tools, Spark, SQL, Python, MongoDB, and AWS S3.

HDFS, Hive, and Sqoop ingestion
Spark processing with Scala
Python analytics and visualization

Outcome

Enabled faster analysis of travel operations and customer behavior, helping the business identify new opportunities and improve operating efficiency.

Improved analytical readiness
Better business visibility
Reusable data foundation

Technology Stack

Big data tools used for storage, processing, and analytics.

Hadoop HDFS Hive Sqoop Spark Scala Python SQL MySQL MongoDB AWS S3

Multi-Source Customer, flight, financial, and operational data

Cloud AWS S3 based storage and analytics support

Spark Distributed processing for large datasets