About the Client
The client is a prominent gaming company operating under a well-established brand with a strong presence across Europe and Asia. Focused on delivering high-quality gaming experiences, the organization relies on data-driven insights to optimize operations, strengthen customer engagement, and support sustainable business growth.
The Background
As the client expanded its operations and introduced a new brand, the need for a more advanced and scalable gaming platform became increasingly important.
The transition was designed to streamline operations, improve efficiency, and strengthen the organization’s ability to leverage data for faster, more informed decision-making. A key requirement was ensuring that data from the new gaming platform could be seamlessly integrated into the client’s existing Enterprise Data Warehouse (EDW) on Snowflake without disrupting ongoing business operations.
The Challenge
The client needed to integrate data from a newly launched brand into its existing Snowflake Enterprise Data Warehouse while simultaneously transitioning to a new gaming platform. The project presented several technical and operational challenges.
Time Sensitivity: The migration and integration needed to be completed within a tight timeframe to minimize disruption to business operations.
Resource Constraints: The client’s internal team had limited bandwidth and required additional technical expertise to execute the transition efficiently.
Data Structure Differences: The new gaming platform introduced a different data model, requiring careful mapping and transformation before the data could align with the existing EDW architecture.
Data Accuracy: Maintaining consistency between source and target systems was essential to preserve data quality and ensure reliable analytics and reporting.
Limited Platform Familiarity: As the gaming platform was newly introduced, the team had limited familiarity with its underlying data structures, dependencies, and integration requirements.
To address these challenges, the client partnered with Supply Medium to deliver a seamless, efficient, and scalable data integration solution.
The Solution
Supply Medium developed a structured and scalable integration strategy using AWS services, Snowflake, dbt, and Apache Airflow.
An Agile delivery methodology supported an iterative and collaborative implementation process, allowing the team to respond quickly to emerging requirements, resolve unforeseen challenges, and maintain close stakeholder alignment throughout the project.
1. In-Depth Analysis & Strategic Planning
The team analyzed the new gaming platform’s database alongside the existing EDW to identify schema differences, dependencies, and integration requirements.
Data fields, relationships, and transformation requirements were mapped to ensure compatibility with the existing Snowflake data warehouse structure.
Based on these findings, Supply Medium designed a scalable data pipeline architecture capable of supporting the immediate migration as well as future growth.
2. Data Engineering & Pipeline Development
Supply Medium developed robust ETL pipelines to extract, transform, and load data from the new gaming platform into the client’s data ecosystem.
AWS Database Migration Service (DMS) was used to efficiently migrate source data into Amazon S3, which served as an intermediate data lake and storage layer.
dbt was used to structure and transform data from staging into operational datasets before integration with the Enterprise Data Warehouse.
Apache Airflow provided pipeline orchestration and automation, supporting data updates every 15–30 minutes and enabling near real-time data availability.
SQL stored procedures were also developed for complex transformations, helping maintain data integrity while ensuring alignment with established business rules.
The Results
The integration successfully connected the new brand’s data with the existing Snowflake environment while maintaining business continuity throughout the transition.
Near real-time data updates gave stakeholders faster access to actionable insights and supported more responsive strategic decision-making.
Centralized and consistent data improved reporting accuracy and efficiency across the organization.
The use of AWS services provided a scalable and cost-efficient infrastructure capable of handling growing data volumes without unnecessary infrastructure overhead.
The flexible pipeline architecture also established a strong foundation for future expansion, making it easier to integrate additional brands, platforms, and data sources.
Post-Implementation Support & Knowledge Transfer
To support long-term success, Supply Medium provided comprehensive post-implementation assistance and knowledge transfer, enabling the client’s internal team to confidently manage and optimize the new data environment.
The team provided two months of post-integration support to resolve early-stage technical issues, optimize performance, and ensure platform stability.
Knowledge Transfer (KT) sessions delivered hands-on guidance for managing, troubleshooting, and optimizing the data pipelines.
Reverse KT sessions were also conducted to validate knowledge transfer, identify remaining gaps, and ensure the client’s team was prepared to manage the solution independently.
Client Feedback
The client highly valued Supply Medium’s technical expertise, adaptability, and proactive approach throughout the engagement.
The team was recognized for effectively navigating technical uncertainties, maintaining transparent communication, responding quickly to evolving requirements, and delivering a high-quality solution within the required timeframe.
One Response
Hi, this is a comment.
To get started with moderating, editing, and deleting comments, please visit the Comments screen in the dashboard.
Commenter avatars come from Gravatar.