About the Client

The client is one of Germany’s largest insurers as they are Germany’s fourth-largest household insurer and fifth-largest car and seventh-largest liability insurer. The company planned to establish a new BI and analytics landscape based on Amazon Web Services.

Problem Statement

The client had relied on a data warehouse based on Oracle, Informatica and MicroStrategy. The system was to be used as a central analytical database.
The problem being:

  • The client considered the system to be very complex
  • The layer model and data structures could be significantly simplified
  • The usage was low and there were only a small number of standard reports
  • There was a need to create a common database 

Therefore, it was decided to build a new BI and Analytics landscape based on an AWS build.

The Challenge

The client needed a new production system to be created. For this:

  • They were looking for a company that could help them implement the system 
  • The client wanted the contractor to give feedback on the statements and assumptions made 
  • The company expected a comprehensive, best-practice consulting service which needed the best-practice variant to be demonstrated to the client
  • The client needed expertise to be able to carry out the project successfully

The Solution

The project is divided into a design phase and an implementation phase:

  • The foundations are laid for later implementation 
  • Likewise the coordination and refinement of the architecture and the tool stack was needed as well as the standards and guidelines for the development

The consulting and the trainings of Scalefree had an impact on the project regarding:

  • Architecture concepts
  • EDWH Strategy
  • Data Warehouse Automation
  • Empowerment
  • Project Support

Project procedure illustration :

Tangible Results for the Client

The following results are expected by the client:

  • Simplified access to the data overview 
  • Development of a GDPR-compliant data infrastructure 
  • Implementation of a dashboard for multi-temporal analysis
  • Securing network resources against unauthorized access
  • Knowledge transfer through knowledge management systems and workshops with users
  • Clean documentations and guidelines about the integrated System 

Suggested architecture:

Technologies used

  • Loading data from the source into the data lake: Python
  • Snowflake DB
  • Transfer of data from the data lake to the EDW layer: Snowpipe
  • Transfer of data to the information mart: implemented with Snowflake SQL Views

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