Overview
We build robust, scalable data pipelines that reliably integrate heterogeneous source systems — from classic ETL with IBM DataStage and Microsoft SSIS through cloud-native solutions with Azure Data Factory and GCP Dataflow to big-data processing with Apache Spark.
Data quality rules are integrated directly into the pipeline so errors are caught early and automatically cleansed.
Whether batch processing or near-real-time streaming — we deliver the architecture that matches your data volumes and latency requirements.
Typical use cases
- Consolidation of heterogeneous data sources into a unified data model
- Automated data quality checking and cleansing
- Migration from on-premises ETL to cloud-native pipelines
- Near-real-time data integration for operational dashboards
- Data lake ingestion from ERP, CRM, and external sources
Technologies
More focus areas
Data Warehousing & Lakehouse
Central data models with SAP DWC, BigQuery, Microsoft Fabric and Hadoop/Cloudera.
Learn more →BI & Visualization
Dashboards and reporting with Power BI, Tableau, Superset, Oracle OBI, QlikSense and SAP SAC.
Learn more →Machine Learning & AI Analytics
Forecasting, segmentation and ML models — including SAP analytics scenarios.
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