A company that developed a platform aimed at increasing eCommerce sales through the recovery of abandoned shopping carts struggled with handling the increased data volume on its current hosting. Consequently, the company decided to migrate to AWS but wanted to do so incrementally to ensure the integrity of the service was not jeopardized.
The client’s SaaS business, which sends physical letters for online stores, outgrew its initial small-scale Windows setup due to increased data volumes. Migrating to AWS, they chose an incremental strategy to minimize risks, costs, and downtime, ensuring a smooth transition without disrupting service.
The first step in the incremental AWS migration involved moving the system’s core to the new infrastructure while maintaining connectivity with components still on the old setup. This approach aimed to keep the system’s most crucial and maintenance-heavy part running on AWS, reducing costs and minimizing service disruptions during the transition.
The Addepto Team started by thoroughly reviewing the existing data pipeline and Data Operations, aiming to pinpoint inefficiencies and bottlenecks while setting actionable steps for improvement. Their strategy focused on adopting AWS best practices for deploying, monitoring, and governing data services to ensure scalability.
Recognizing that migration involves more than just relocating services due to differences in technology and architecture, the process demanded significant code revision and, often, complete rewriting to achieve compatibility and efficiency in the new environment.
The client’s business was based on a SaaS platform that generates and sends traditional letters to consumers of online stores as a replacement for standard reminder emails, utilizing sales data.
As the business grew, this system proved insufficient to handle the increasing data volume, and upgrading to a higher package was not cost-effective. The company thus decided to migrate to AWS.
However, since migration is always a risky, time-consuming, and costly operation, they aimed to ensure the entire process would be safe and smooth without a significant amount of downtime during which services would be unavailable to users.
Our newly developed system enables all the microservices required for PDF generation to operate in parallel, dramatically slashing the time that was required to generate a batch of PDFs. This innovation not only boosted efficiency, resilience and fault-tolerance, but also elevated scalability. Now, it can handle huge spikes of job load in a quick manner, which could cause the old system to time out.
The Addepto Team began with a comprehensive examination of the existing data pipeline architecture and the Data Operations processes. This evaluation was aimed at identifying any inefficiencies and bottlenecks, as well as at setting clear, actionable recommendations for refining the data pipeline and establishing a streamlined, efficient Data Operations process.
The recommendations were based on best practices for deploying, monitoring, managing, and governing data services and solutions within the AWS ecosystem, ensuring that the approach adopted was scalable and effective for both current and future needs.
The initial step in adopting an incremental migration strategy involved abstracting the core of the service and migrating it without losing connectivity with the rest of the system, which remained on the old infrastructure.
The objective was to ensure that the “heart of the system,” the most maintenance-intensive component, was operational on the new infrastructure while the remaining components stayed on the old one.
This strategy facilitated a smoother transition by prioritizing the migration of the most critical and resource-demanding part of the system, thereby reducing operational costs and minimizing disruptions to the overall service during the migration process.
The deployment of the new solution on AWS resulted in significant cost savings, as the company could adopt a “pay as you go” cloud strategy, paying only for actual AWS usage instead of a flat fee for local server infrastructure.
However, the advantages extended beyond mere financial gains. The data flow saw considerable improvement through the integration of automation, making the system more fault-tolerant, easing the process of reruns, and eliminating logical errors. These enhancements in resilience and efficiency not only streamlined operational processes but also strengthened the overall infrastructure.
As a result, data management and processing became more effective and reliable, representing a major advancement towards a robust and dependable system.
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