ETL Platform with KNIME in Logistics

Starting Point

An international logistics and business process outsourcing provider with offices in 8 countries managed customer data, transaction data, and operational metrics across a fragmented ecosystem of over 15 different source systems:

• ERP systems: SAP and Oracle in different country organizations
• CRM: Salesforce for sales, a legacy CRM for customer service
• Operational systems: Warehouse Management, Transport Management, custom applications
• Flat files: CSV exports from partners and clients, daily delivery files
• APIs: Real-time data from IoT sensors and tracking systems

The consequences:

• No unified customer view — the same company existed in 4 systems with 4 different spellings
• Reports contradicted each other because source systems used different aggregation logic
• Manual data reconciliation consumed 3 full-time positions in the controlling team
• New reporting requirements took weeks because each data source had to be individually connected

Challenge

The requirements for an ETL platform were specific:

• Heterogeneous sources: SQL databases, REST APIs, SFTP files, SAP RFC — everything must be integrable
• Data quality: Automatic cleansing, deduplication, and validation before loading
• Scalability: 50 GB daily data volume today, estimated 200 GB in 2 years
• Self-service: The BI team should be able to create simple ETL workflows themselves — without developers
• Auditability: Every record must be traceable (source, timestamp, transformation steps)
• Cost: No six-figure enterprise ETL license

Solution

We chose KNIME Analytics Platform as the central ETL engine — for three reasons:

1. Open source core: No license costs for the core platform
2. Visual workflow designer: Drag-and-drop workflows that non-developers can also read and modify
3. Connectors: Native support for SAP, Oracle, Salesforce, REST APIs, and file formats

Architecture

Source Systems → KNIME ETL Server → Staging Area → Data Warehouse → BI Layer
    SAP          (Orchestration)     (Raw Data)     (Star Schema)    (Power BI)
    Oracle       (Scheduling)        (Validation)   (KPI Tables)
    Salesforce   (Monitoring)        (Cleansing)
    CSV/APIs     (Error Handling)    (Deduplication)

ETL Workflows

We implemented 35 ETL workflows in KNIME, organized in three layers:

Layer 1: Extraction (12 Workflows)

• SAP extraction via RFC connector (customer master, orders, financials)
• Oracle extraction via JDBC (operational data)
• Salesforce extraction via REST API (leads, opportunities)
• File ingestion via SFTP watcher (daily partner files)

Layer 2: Transformation (15 Workflows)

• Master data matching: fuzzy matching for customer names and addresses (Levenshtein distance)
• Deduplication: identification and merging of duplicates across source systems
• Standardization: unified formats for dates, currencies, country codes
• Calculation: derived KPIs (throughput time, utilization, cost per shipment)

Layer 3: Loading (8 Workflows)

• Incremental loading into the data warehouse (changed records only)
• Star schema population with dimension and fact tables
• Failed records routed to quarantine table with alert

Data Quality Framework

A central component of the solution was the automated data quality framework:

Results

After 8 months in production:

• Data sources integrated: 15 (up from 6 manually connected)
• Data volume: 65 GB daily processing, reliably within the 4-hour load window
• Duplicates resolved: 12,000 customer duplicates identified and merged
• Controlling effort: 2.5 FTE saved through automatic data reconciliation
• Report creation time: from weeks to days for new reports (data is already in the warehouse)
• Data quality score: from an estimated 62% to a measured 94%

Lessons Learned

1. Data Quality Before Integration

We spent the first 6 weeks only measuring data quality — without loading anything. The discovery that 38% of customer data had quality issues fundamentally changed the project scope.

2. KNIME Is Not a Toy

The visual interface tempts the assumption that KNIME is for “simple” tasks. In practice, we implemented complex fuzzy-matching algorithms, parallel processing, and error handling — all visual but far from trivial.

3. Monitoring Is Not Optional

An ETL workflow that silently fails is worse than none at all. We implemented a monitoring dashboard that logs every workflow run: start time, duration, records processed, errors, and warnings.