Success Story

Modernized ETL Landscape for a Leading US-based Airline Company with AWS Glue

About the Client

One of the world’s largest airline companies headquartered in the US

Challenges

  • Faced scalability and performance issues with legacy ETL infrastructure.
  • On-premises systems struggled to process large volumes of data, leading to high latency and operational inefficiencies.
  • Growing data volumes required increased manual intervention, resulting in higher operational costs.
  • Legacy setup lacked seamless integration with modern cloud-based platforms.
    Inability to support real-time, data-driven decision-making due to outdated infrastructure.

Approach

  • Comprehensive Discovery & Planning
  • Tailored Data Migration Strategy
  • Cloud-Native Architecture & Automation
  • Seamless Integration and Governance

Solutions

Innover executed a structured, three-phase modernization strategy to migrate the client’s ETL operations to AWS Glue:

1.Data Discovery: Assessed existing ETL workflows, dependencies, and architecture to identify migration risks and define the transformation scope.

2.Planning & Design: Crafted a comprehensive migration roadmap covering data mapping, transformation logic redesign, and resource alignment for AWS.

3.Data Migration: Executed a lift-and-shift for simple ETL jobs with minimal changes. Re-engineered complex workflows to leverage AWS Glue’s native capabilities for optimized performance.

4.Technical Execution: Adopted AWS Glue’s serverless architecture to eliminate infrastructure management and reduce complexity. Enabled auto-scaling to efficiently handle varying data volumes and minimize manual effort.

5.Integrated seamlessly with Amazon S3, Redshift, and Athena to enhance data accessibility and analytics.

6.Embedded automated data quality checks to ensure accuracy and consistency.

7.Enforced AWS security best practices with encryption, role-based access, and audit logging to ensure compliance.

Impact Delivered

2X

faster data processing through
reduced ETL job runtimes

20%

reduction in operational costs
by eliminating legacy licensing and
minimizing infrastructure management

40%

decrease in manual effort through
automated, scalable data operations