Overcoming legacy database challenges in logistics modernization
The client, an Australia-based logistics leader, connects businesses and consumers through a vast delivery network across road, rail, air and sea. It provides end-to-end freight, parcel and supply chain services nationwide and globally.
The challenge
The organization faced significant obstacles in modernizing a mission-critical application in operation for more than 35 years.
- A highly complex legacy MySQL database with intricate tables and relationships that had evolved over decades
- Complete absence of subject matter experts (SMEs) familiar with the existing database design or business logic
- Limited understanding among DBAs of how the database was functioning, making schema mapping and modernization risky
- Difficulty in planning a migration to the target state without inadvertently disrupting existing processes
- These challenges created a high degree of uncertainty, increasing the risk of project delays and potential data integrity issues
The objective
The primary objective was to enable teams to systematically reverse engineer and understand the structure of the legacy MySQL database. This knowledge was critical to designing the target-state architecture, reducing dependency on missing tribal knowledge and ensuring a seamless migration.
The solution
To overcome the knowledge gap, a GenAI-powered solution was implemented:
- Streamlit-based web UI: A lightweight, user-friendly chat interface was built to facilitate interaction with the legacy schema
- Schema ingestion: The legacy database schema was ingested into the system for contextual analysis
- Bedrock + Claude Sonnet: An Amazon Bedrock-powered Claude Sonnet model was integrated to interpret schema definitions and relationships
- Conversational reverse engineering: Users could query the model to decipher table structures, column purposes and hidden dependencies, enabling step-by-step reconstruction of the database logic
- Iterative discovery: Teams used the chat UI to collaboratively explore the schema until a complete understanding of the legacy system was achieved
The impact
The initiative delivered measurable improvements in efficiency and risk reduction:
- Enabled teams to gain deep insights into a 35-year-old database without the need for legacy SMEs
- Significantly reduced the time required to reverse engineer and document complex schema structures
- Provided a collaborative, conversational interface that democratized access to technical knowledge
- Minimized migration risks by ensuring the target-state design was based on a solid understanding of the existing system
- Created a repeatable AI-driven framework that could be applied to other legacy systems in the future
AWS services used
- Amazon Bedrock
- Amazon ECS
- AWS CloudTrail
- Amazon CloudWatch
- Amazon VPC
