Case Study: Revolutionizing Data Management for Atruvia with Open Source Solutions
Client: Atruvia (IT Provider for Volksbank and Sparkasse)
Project Overview:
Atruvia, the IT backbone for Volksbank and Sparkasse, was facing escalating costs and limitations with their Hadoop-based data management infrastructure. Recognizing the need for a more cost-effective and advanced solution, Atruvia sought to build a modern data warehouse leveraging cutting-edge technologies. The goal was to create a BaFin-compliant microservice architecture that empowers analytics teams to handle massive datasets with ease, using only open-source tools and avoiding any public cloud components.
Objective:
To replace the expensive Hadoop infrastructure with a scalable, efficient, and cost-effective data warehouse solution built on Trino and S3 autoscaling clusters, ensuring compliance with BaFin regulations and optimizing data performance for end-users.
Solution Design Process:
- Requirement Analysis:
- Conducted in-depth discussions with Atruvia’s IT and analytics teams to understand their specific needs, challenges, and regulatory requirements.
- Identified critical aspects such as cost reduction, scalability, data performance, and ease of use for analytics teams.
- Technology Evaluation:
- Evaluated various open-source technologies to replace Hadoop, focusing on Trino for its powerful SQL query capabilities and S3 autoscaling clusters for efficient data storage.
- Ensured all selected technologies were compliant with BaFin regulations and could be seamlessly integrated into Atruvia’s existing infrastructure.
- Architecture Design:
- Designed a microservice architecture using OpenShift to host the entire data warehouse and analytics environment.
- Implemented S3 autoscaling clusters as the primary storage solution, replacing traditional databases and ensuring scalability for huge datasets.
- Developed a BaFin-compliant framework to manage data security and regulatory compliance.
- User-Friendly Tools and Environments:
- Created pre-configured Jupyter Notebook environments to enable analytics teams to upload, analyze, and visualize large datasets without needing extensive technical knowledge.
- Integrated interactive dashboards to provide real-time insights and streamline data analysis processes.
Implementation:
- Infrastructure Setup:
- Deployed Trino and S3 autoscaling clusters within the OpenShift environment, ensuring high availability and scalability.
- Configured the microservice architecture to handle data ingestion, processing, and querying efficiently.
- Data Migration:
- Executed a seamless migration of data from the Hadoop infrastructure to the new Trino and S3-based data warehouse.
- Ensured data integrity and compliance throughout the migration process.
- User Training and Support:
- Provided comprehensive training sessions for the analytics teams to familiarize them with the new tools and workflows.
- Established a support framework to assist users in transitioning to the new environment and maximizing its benefits.
Results:
- Cost Reduction: Successfully reduced data management costs by replacing the expensive Hadoop infrastructure with a more efficient open-source solution.
- Scalability and Performance: Achieved significant improvements in data scalability and performance, enabling seamless handling of massive datasets.
- Regulatory Compliance: Ensured full compliance with BaFin regulations, providing a secure and reliable data management environment.
- User Empowerment: Empowered analytics teams with easy-to-use tools, eliminating the need for PySpark and complex configurations, and enabling them to focus on deriving insights from data.
Conclusion:
The project resulted in a transformative data management solution for Atruvia, leveraging open-source technologies to deliver a scalable, cost-effective, and BaFin-compliant data warehouse. By replacing Hadoop with Trino and S3 autoscaling clusters, and providing user-friendly analytics tools, Atruvia significantly enhanced its data capabilities, ensuring optimal performance and empowering its analytics teams.
