Executive Summary
A leading global airline needed a scalable way to convert IFE BITE data into reliable reporting and operational analytics. Each reporting cycle previously took nearly 16 hours, leveraging four employees working across multiple reports to scrape, cleanse, validate, and visualize the data.
LTTS delivered an automated data pipeline, medallion data architecture, centralized warehouse, and Power BI reporting layer. Helping reduce the cycle time by 25-50% across analyst-led review and validation, the solution supports about 714 aircraft leveraging a single vendor web application as the primary data source.
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Download Case StudyThe Challenge
The client relied on a manual, effort-intensive workflow to extract IFE BITE data, and then clean, validate, and prepare it operational reports. This resulted in significant challenges for the airline, including:
- High effort of about 16 hours
- Scalability challenges across the need for supporting an over 700-strong aircraft fleet without added effort
- Data quality gaps with the need for extensive cleansing, validation, and curation
- Processing limitations across rising offload volumes reduced real-time reliability
- Delayed insights, with limited visibility into compliance, reliability, and asset health
Engineering Complexity
Layered data conversion
Raw vendor data had to move through Bronze, Silver, and Gold layers before becoming dashboard-ready
Built-in quality controls
Bronze standardized ingestion; Silver handled cleansing, validation, deduplication, and transformation; Gold created curated datasets for analytics
Architecture shift
Rising log volumes required a move from near real-time to batch processing for stability and scale
Operational dashboard needs
Media, Configuration, and Offload reports had to serve different users without duplicating processing logic
Actionable reliability analytics
Historical BITE and aircraft operational data had to reveal recurring failure patterns, reliability trends, and component health indicators
Vendor data integration
Established a reporting and analytics foundation using data from a single vendor web application
Automated pipeline
Used AWS for ingestion, storage, and processing, with AWS Glue for ETL and Python for extraction transformation, and automation
Medallion architecture
Implemented Bronze, Silver, and Gold layers to improve data quality, completeness, reliability, and usability
Centralized warehouse
Stored curated Gold-layer datasets in Amazon Redshift for scalable reporting and analytics
Power BI dashboards
Delivered Media, Configuration, and Offload views covering media status, software and system configuration, BITE offload visibility, and compliance tracking
Batch processing model
Replaced near real-time processing with batch processing to reduce failures and handle higher flight offload log volumes consistently
Prediction and recommendation layer
Connected historical BITE and aircraft operational data with reliability analytics to support failure prediction and proactive replacement recommendations
Controlled delivery
Used GitLab for source control and CI/CD to support governed development and deployment
Business Outcomes
LTTS improved the speed, reliability, and usability of IFE BITE reporting by replacing manual data preparation with an automated pipeline, curated warehouse, and dashboard-led insight layer.
~16 hrs to ~2-3 hrs
Automated BITE reporting reduced manual effort
~714
Aircraft
Enabled fleet-scale reporting and analytics
3
Dashboards
Delivered Media, Configuration, and Offload insights
Trusted data
Improved data quality through Bronze-Silver-Gold architecture
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