The pipeline nobody wants to touch
Every enterprise has that one ETL job — built five years ago by someone who left, running on a server nobody manages, transforming data through logic nobody understands. It breaks on public holidays, fails silently on schema changes, and the team responsible finds out when the Monday morning report is missing.
Modern ETL/ELT pipelines should be version-controlled, tested, monitored and self-healing. They should handle schema evolution, track data lineage and alert on quality issues — not just move bytes from A to B.
Map & Design
We map source systems, transformation logic and target schemas. Pipeline architecture is designed for reliability, scalability and maintainability.
Build & Test
Pipeline development with unit tests for transformation logic, integration tests for data flow and reconciliation checks for accuracy.
Monitor & Maintain
Pipeline observability with SLA tracking, failure alerting and automated recovery. Proactive maintenance, not reactive firefighting.
What we deliver
Enterprise-grade data integration with ADF: parametrized pipelines, linked services, integration runtimes and monitoring. Complex orchestration made manageable.
Maintaining and modernizing existing SSIS packages. Incremental migration to cloud-native alternatives while keeping data flowing.
SQL-first transformation with dbt: version-controlled models, automated tests, documentation and lineage. The modern standard for analytics engineering.
Large-scale data processing with Apache Spark on Databricks. Handling terabytes of data with Delta Lake for ACID transactions and time-travel queries.
Real-time CDC from SQL Server, Oracle, PostgreSQL and MySQL. Streaming changes to downstream systems without impacting source database performance.
SSIS pipelines aging or data warehouse always behind?
Talk to a pipeline engineer who migrates and modernizes ETL at enterprise scale.
Arvato Turkey
Multiple disconnected data sources requiring manual data preparation. Analysts spent 60% of their time cleaning data instead of analyzing it.
We built automated ETL pipelines with KNIME and Azure Data Factory, standardizing data from 6 sources with automated quality checks and scheduled orchestration.
Analysts now spend 90% of time on analysis, not preparation. Data refresh from daily manual to hourly automated.
Where we apply it
- Modernize aging SSIS packages without data gaps
- Rebuild your ETL layer for 10x faster data refresh
- Stream operational data changes in real time with CDC
- Consolidate data from multiple sources into one pipeline
- Migrate data platforms with zero record loss
Technologies
Microsoft 
Cloudera