About the customer
As a publicly traded family business, the Fielmann Group has over 900 branches in Germany and Europe, supplying more than 27 million customers across Europe with eyeglasses, contact lenses, and hearing aids. In addition to its exceptional customer focus and fair prices for its products, Fielmann has been impressing customers for decades with its high quality and excellent service. Fielmann is the price leader and regularly sets standards for new technological developments within the industry.
The challenge:
The Central European market leader supplies both brick-and-mortar and online retailers via an omnichannel business model with digital sales channels. Different product teams in various departments across the entire customer organization work on collecting, evaluating, and providing data from the sales and e-commerce systems.
The Fielmann Group has established a central data analytics team that makes various evaluations and dashboards easily accessible and usable within the customer organization. The goal is to enable the business to make data- and insight-driven decisions. The “one-stop shop” concept is pursued here, making all relevant data available in one central location.
Depending on the analysts' skills, there are opportunities here to prepare data yourself, perform efficient ad hoc evaluations, create management reports, or gain new insights with machine learning.
The omni-channel business model and the associated digital transformation require an agile, scalable, and high-performance analytics architecture. In addition to new data sources (data mesh architectures, streaming data, APIs), legacy applications (classic databases) must also be efficiently integrated into the mapping of the entire customer journey, both online and offline. Furthermore, the requirements for the availability of information from a wide range of stakeholders (controlling & finance, sales, marketing, logistics, etc.) must be mapped.
The solution
Implementation of the solution architecture: “Analytics Lakehouse”
PROTOS Technologie GmbH supports the analytics department in planning and automatically provisioning the AWS infrastructure for various ETL pipelines, providing ready-to-use data sources, and performing exploratory data analysis in order to optimally supply stakeholders with enriched data. PROTOS supports the migration of existing infrastructure components, data services, pipelines, and data artifacts to the new architecture.
In order to implement processing with big data frameworks and modern database technologies in a sustainable manner and for a wide variety of data producers and consumers, existing analytical data pipelines are being migrated and converted from a data warehouse concept to an “analytics lakehouse architecture” based on AWS services (AWS S3, AWS Redshift, AWS Glue, AWS Lambda).
In addition, PROTOS provides components for simple, department-specific, and efficient data handling, such as dbt for data provision and state-of-the-art reporting tools for visualization and ad hoc analysis.
Infrastructure provisioning: “Automation”
The modernized infrastructure is provisioned using Infrastructure as Code (IaC), ensuring consistency and version control. AWS CDK and Hashicorp Terraform are used for this purpose. By using AWS managed services such as AWS Codebuild, the CICD infrastructure does not need to be maintained manually.
The automated deployments for the individual components and the customization of infrastructure and ETL jobs are created using proven DevOps practices (AWS Codebuild + Codepipeline, GitHub Actions). The high degree of automation and the potential of serverless/elastic cloud architectures is enhanced by the use of AWS Elastic Container Service (ECS) and AWS Lambda in conjunction with AWS Step Functions.
Implementation of data pipelines
To enable different data consumers to work efficiently with the data, the focus is on high data quality by enriching the data and bringing it into an analytics-friendly structure. PROTOS supports Fielmann's analytics team in particular in the development of data pipelines and ETL jobs. Spark on AWS Glue, AWS Redshift, and in combination with AWS Lambda are used for this purpose. This provides the product teams with ready-to-use data sources.
