Arvato Bertelsmann

Building a repeatable KNIME ETL workflow for Arvato Bertelsmann

Fragmented data sources can slow reporting and make quality controls hard to repeat. Deka Technology built a KNIME-based ETL workflow for cleansing, validating, enriching and loading file and database data.

Primary service
Data & AI
Secondary capabilities
  • Enterprise Integration

Project overview

Client
Arvato Bertelsmann
Focus
Data integration and ETL automation
Service areas
Data & AI; Enterprise Integration within Software Engineering & Modernization
Source formats
Excel, CSV and databases
Target environment
Microsoft SQL Server

Challenge

Business data was distributed across Excel files, CSV exports and database systems. Preparing these sources for downstream use required more than moving records from one system to another: formats had to be aligned, quality rules applied and errors handled consistently.

The project needed a repeatable integration workflow that could standardise incoming data and apply consistent processing requirements.

Deka Technology role

Deka Technology designed and implemented the KNIME-based ETL workflow. The work covered source extraction, transformation logic, data-quality controls, enrichment, error handling and loading into Microsoft SQL Server.

The solution was structured for regular automated execution rather than one-off data preparation.

Approach

  1. Connect the source landscape

    KNIME connectors were used to bring together Excel, CSV and database inputs.

  2. Standardise the data

    Cleansing, data-type conversion and merge transformations created a consistent processing layer.

  3. Apply quality controls

    Validation rules checked records before loading, while custom scripting supported required enrichment logic.

  4. Handle exceptions

    Error-handling steps made failed or incomplete records visible within the workflow.

  5. Load and operationalise

    Prepared data was loaded into Microsoft SQL Server, and the ETL flow was configured for regular automated execution.

Outcomes

The delivered workflow supported a consistent approach to preparing and integrating data across the selected sources.

It provided standardisation and validation before loading, structured handling of incomplete or failed records, regular automated execution and data preparation for analysis and decision support.

Technology

  • KNIME
  • Microsoft SQL Server
  • PostgreSQL

Deka Technology

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