Atruvia / Sparkasse / Volksbank – Data Warehouse

Replacing Hadoop with a Data Warehouse built on top of Trino, built with an autoscaling microservice architecture to handle finance data of millions of German customers.

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.

Are You Looking to Modernize Your Data Infrastructure? Contact us today to discover how we can help you build a scalable, cost-effective, and compliant data management solution tailored to your needs!