How we turned a chaotic legacy system into a scalable data product in 5 months
A company that helps colleges and universities improve enrollment through a combination of data analytics, marketing, and technology. They support institutions across the full student lifecycle, from recruitment to retention.
Key achievements
Key achievements
| 20+ | clients onboarded |
| <30% | development cost savings |
| ~30% | infrastructure cost reduction |
Challenge
An existing data platform had become difficult to maintain and scale, which led the client to seek support in modernization. The system, built on Microsoft Azure, had been in place for years and no longer supported new requirements.
This situation began to impact the business directly. Product quality declined, and day-to-day operations slowed due to the outdated stack and unclear system logic. Plans to onboard new customers and expand the offering faced delays, as the platform could not support these goals reliably.
To address these challenges, external expertise was brought in to assess the current state and define a clear legacy modernization path forward. The focus was on stabilizing the platform and building a foundation for future growth. Close collaboration with internal stakeholders helped clarify constraints and align priorities.
Several issues affected progress from the start.
Lack of system clarity
The legacy platform lacked documentation, and internal teams could not fully understand parts of the system logic. The data structure had evolved over time, which made the system more difficult to maintain and extend.
Unclear infrastructure direction
The client needed a flexible solution without dependence on a single cloud provider. At the same time, the team had to evaluate IBM Cloud, AWS, and Databricks without a final infrastructure decision in place.
Restricted access and slow onboarding
Strict security policies introduced multiple approval layers. This slowed onboarding and limited access to data, which delayed early progress and reduced team efficiency.
Solution
The client needed a scalable legacy software modernization to replace an Azure-based system and support growing data operations across multiple institutions. The goal was to update the platform within its existing ecosystem, improve interoperability between components, support flexible data flows and customization, and reduce reliance on a single cloud provider.
They turned to the Aristek team for deep technical expertise to assess the situation, develop a scalable data product, and guide key decisions. From the start, the team worked closely with the client’s architects to evaluate technology options, including Databricks and multi-cloud approaches.
A dedicated team of data engineers and DevOps specialists, supported by a project manager, was assembled within one month. In the initial phase, the team reverse-engineered the legacy system to recover missing knowledge and define a clear migration path.
The resulting solution was implemented on AWS and includes the following components:
End-to-end data pipelines
Automated pipelines process data from ingestion through transformation, validation, and delivery. The design supports consistent data flow across systems and reduces manual intervention.
Flexible data ingestion layer
Supports databases, CSV and JSON files, and REST APIs with authentication and pagination. The approach allows the platform to integrate with multiple data sources without additional complexity.
Custom transformation and mapping logic
Implements business rules, data mapping, enrichment, and validation. The structure improves consistency and makes future updates easier to manage.
Automated data delivery
Processed data is exported, sent to downstream systems, or returned to the client. This reduces delays and supports reliable data distribution.
Event-based orchestration
Triggers and workflows run automatically when new data appears. This enables faster processing and reduces dependency on manual execution.
Infrastructure flexibility
The architecture was designed based on a joint evaluation of AWS, Databricks, and other options. The final setup supports future migration and avoids reliance on a single provider.
Security and access management
Credentials are managed through AWS Secrets Manager with secure authentication practices. This aligns with enterprise security requirements and controlled access policies.
Code quality processes
Development includes linters, mandatory code reviews by two engineers, and GitHub-based version control. These practices improve maintainability and reduce the risk of errors.
Project scope
The team integrated into the client’s workflows, tools, and communication channels, allowing the internal team to stay in control. A daily overlap of 2 to 4 hours with US-based stakeholders supported alignment despite the time zone difference, with project management ensuring clear coordination and communication.
The project was divided into the following key stages:
1. Discovery phase & system analysis |
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2. Architecture design |
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3. MVP development |
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4. Testing & validation |
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5. Deployment & launch (1 month) |
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6. Ongoing support & improvement |
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How it works
The system operates as an automated data pipeline with event-driven processing. The client provides data in formats such as CSV, Excel, or other files. An SFTP server is monitored for new uploads. When new files appear, a pipeline is triggered.
Files are received and copied from the source (e.g., SFTP server) into the system.
Data is extracted from source formats such as CSV, Excel, or other files.
Transformation and mapping logic are applied based on client-specific rules.
Data is validated, enriched, and prepared for further use.
Processed data is stored in internal storage.
Data is distributed to target systems or returned to the client as downloadable files.
In parallel, data can be used for analytics and visualization (e.g., Power BI).
Team
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Data Engineers x6
![Image of slide 1]()
DevOps Engineers x2
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Project Manager x1
Tools & technologies
Project results
Project results
 | 5 months from kickoff to launch |
 | 20-30% infrastructure cost reduction |
 | Up to 30% development cost savings |
 | 20+ clients onboarded The new setup carried over 20 clients from the legacy platform into a cleaner operating model. It provided a more efficient way to manage client data. |
Key takeaways
The project replaced a legacy setup with a working platform for one part of a larger system. Data now flows through SFTP monitoring, automated processing, validation, and storage before reaching downstream use cases such as client downloads and Power BI.
Access was granted in stages, so the team started with test data and switched to real inputs once approvals were in place. With 2 to 4 overlapping hours with US-based teams, handoffs and reviews remained consistent across time zones.
The result is a stable setup with more efficient operations, lower costs, and a clear path for onboarding new clients.
Further development scope:
Expanding data sources
New integrations will support additional data inputs and simplify the onboarding of new clients.
Enhancing transformation logic
The team will extend business rules and data mapping to support more complex use cases.
Strengthening monitoring and stability
Improvements will focus on faster issue detection and more reliable system performance.
If your system is holding you back, it might be time to rethink the approach.
We can help you shape a clear solution and next steps.