Arvato Turkey, a global retail-services provider, struggled with enormous data diversity, volume, and quality issues. Data from various source systems came in different formats, encodings, and quality levels. Manual data cleansing w-consuming and error-prone, and existing integration processes did not scale with growing data volume.
We implemented a KNIME-based ETL solution that standardizes and automates the entire integration process from extraction through transformation to loading. Data quality rules were integrated into the pipeline so errors are caught early and automatically corrected. The solution is modular and scales with data volume.
KNIME-based ETL platform with visual pipeline modeling. Integrated data quality checks with automatic cleansing and error logging. Standardized connectors for heterogeneous source systems. Scalable architecture that grows with data volume. Consistent, accurate data processes foundation for downstream analytics.
A reliable data foundation for better decisions in a competitive market. Data quality issues are automatically detected and cleansed instead of manually post-processed. The scalable architecture grows with the business — new sources can be connected quickly.
The key building blocks of this solution at a glance.
ETL Pipelines
KNIME-based, visually modeled pipelines for extraction, transformation, and loading from heterogeneous sources.
Data Quality Engine
Rule-based checking, cleansing, and logging of data quality issues directly in the pipeline.
Source Connectors
Standardized adapters for various source systems, formats, and encodings.
- Visual pipeline modeling with KNIME for transparent ETL processes
- Automatic data quality checks with error logging
- Standardized connectors for heterogeneous source systems
- Scalable architecture for growing data volume