1-Minute Deployments in 30 Days: Rebuilding a Legacy Data Platform for Scale
A multi-site U.S.-based service organization was experiencing growing friction with its legacy data platform. As the business scaled, its infrastructure started to show signs of strain. Workflows were fragmented, deployments were manual and error-prone, and technical debt accumulated over time. The data team found themselves focused more on operational firefighting than on innovation.
That’s when they partnered with The Scalable Way to rearchitect their data platform. The goal: streamline operations, reduce overhead, enable automation, and lay the groundwork for long-term scalability and self-service capabilities.
The results? In just 30 days, the client moved from a fragmented system to a modern, automated platform built for scale. Deployments now take less than a minute, workflows run on secure cloud infrastructure, and pipelines are fully templatized and reusable. With 99.11% success, zero incidents, and 100% uptime, the platform is stable, fast, and ready for growth.
Here’s how we made it happen.
The Challenge: Legacy Systems Holding Back Growth
Operating across dozens of physical locations, the client depends on real-time, data-driven decisions to keep operations running smoothly. However, as the organization matured, its existing data setup gradually became a bottleneck.
A few core areas stood out:
- Scalability constraints: The architecture had limited ability to handle increasing data volume and complexity.
- Deployment hurdles: Inconsistent, manual processes introduced friction and slowed delivery.
- Lack of isolated environments: Every change had to be carefully managed in production without a dedicated development space.
- Manual overhead: Routine tasks, like registering new pipelines, relied on hands-on work, which increased time and risk.
- Outdated tooling: A lack of modern Python workflows and tooling made development and onboarding less efficient.
It was clear that supporting day-to-day operations and long-term growth would be an uphill battle without foundational changes.
The Strategic Approach: Build for Today, Design for Tomorrow
When The Scalable Way came onboard, the mission was clear: modernize the platform to meet today’s operational demands while making it scalable and sustainable for the future. Together, we adopted a phased approach to tackle the transformation methodically.
Phase 1: Platform Review and Roadmap
We started by digging into the current setup, trying to understand where the pain points were and where the opportunities lay.
Key activities included:
- Reviewing network setup, VM environments, and infrastructure-as-code.
- Auditing the Prefect orchestration for deployment and automation gaps.
- Analyzing workflows, source code, and release processes.
This deep dive surfaced actionable insights, which we compiled into a centralized GitHub repository, complete with clear documentation (via GitHub Pages powered by MkDocs) and a strategic plan.
The roadmap prioritized:
- Standardization: A unified repo for flow templates, YAML deployments and infrastructure.
- Environment separation: Isolated development and production environments to reduce risk.
- CI/CD adoption: Automated Prefect deployments to remove manual steps.
- Templating & abstraction: Reusable components via the open-source dlt library to speed up development and simplify onboarding.
This wasn’t just a tech upgrade but a move toward sustainable, independent data operations.
Phase 2: Implementation and Automation
With the roadmap in place, the second phase focused on executing the core changes and delivering a modern, automated data platform.
The new infrastructure was deployed within a few days on Google Cloud Platform as a minimum viable product (MVP), using a dedicated Virtual Private Cloud (VPC) setup, with the full data platform completed in under a month. Key components included Compute Engine, Kubernetes, Cloud Storage, and Cloud NAT, all integrated and secured via Identity-Aware Proxy (IAP). From the start, separate dev and prod environments ensured smoother deployments and safer testing.
The team restructured existing Prefect flows using standardized templates and fully automated CI/CD pipelines. This allowed them to roll out new deployments within seconds on the development environment, with an automated way to register them on production once pull request is tested and approved. Instead of maintaining a dozen separate flows, we designed two reusable, generic flows powered by dlt: one handling the EL (extract and load) part of ELT, and the other responsible for the T (transform stage). This use of dlt helped abstract common logic and reduce time spent writing standard code.
We also configured site-to-site VPN connectivity between GCP and internal resources to ensure secure access to on-premises systems.
Behind the scenes, automation and infrastructure improvements made workflows faster, more resilient, scalable, and easier to manage.
The Impact: Efficiency, Security & Team Agility
The results spoke for themselves. In just a few months, the client went from reactive operations to proactive development:
- Scalability: The platform now supports the addition of new workflows and data sources with minimal engineering effort.
- Faster delivery cycles: CI/CD automation significantly reduced time-to-deployment and the risk of manual errors. Prefect deployments were split to handle individual tables instead of entire data sources, allowing quick retries for failed tables without rerunning the whole pipeline. Deployments are fully automated and take about one minute to merge from a pull request.
- Reliability: Environment separation ensured safe testing and consistent production behavior, reducing operational incidents.
- Security improvements: All services run securely within a private cloud setup, with no exposure to the public internet.
- Broader team enablement: A self-service approach empowered more team members to participate in pipeline creation. New deployments now require just a few lines of configuration and can be live in minutes.
- Improved knowledge sharing: Up-to-date platform and deployment documentation made knowledge easier to share across teams, supporting onboarding and continuity.
Lean, Efficient Resourcing
Rather than building a dedicated in-house platform team, the client adopted a fractional team model managed by The Scalable Way. This structure provided all the necessary expertise at a fraction of the cost of traditional hiring.
| Role | Time Allocation |
|---|---|
| DevOps Engineer | 30% |
| Data Platform Engineer | 50% |
| Project Manager | 10% |
| Data Analyst | 10% |
This model enabled rapid delivery and iteration while minimizing both recruitment risk and overhead.
Ongoing Partnership for Long-Term Success
The platform was designed not as a static system, but as a flexible and evolving foundation. The Scalable Way continues to provide support, helping the client:
- Maintain production stability and respond to issues quickly
- Implement necessary security updates and patches
- Adapt CI/CD and workflow logic to meet new project needs
- Provide guidance on new initiatives and changes in the data landscape
This partnership approach ensures the platform remains relevant and valuable as the organization grows and its data needs evolve.
Looking Ahead: A Future-Ready Data Platform
By combining expert consulting with hands-on implementation, we helped our client transform a legacy data stack into a robust, modern platform. Today, their internal teams can move faster, operate more independently, and focus on driving business value rather than maintaining brittle systems.
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