/work
End-to-End Analytics Data Pipeline
[ overview ]
Summary
Designed and implemented a full end-to-end analytics pipeline covering data extraction, transformation, semantic modeling, and reporting.
Context
Multiple external providers delivered sales-out data through APIs and cloud data platforms that needed to be integrated into a single analytics layer with predictable behavior and acceptable performance. 14 source systems, 1B+ rows processed over 4 months.
Solution
Built a modular analytics pipeline spanning API connectivity, source system integration, upstream transformation, Power BI dataset optimization, and report delivery.
Outcome
Reliable and scalable analytics delivery with controlled data processing, optimized datasets, and consistent business reporting across 14 integrated sources.
[ tools & formats ]
[ implementation details ]
Data extraction
External sales-out data from 14 source systems is accessed using custom Python-based API clients and direct connections to cloud data platforms. The pipeline processes over 1 billion rows, built and delivered within 4 months.
BigQuery and other cloud systems act as source systems rather than managed storage layers.
- Python-based API connectivity
- Multiple external data providers
- Cloud data platforms used as upstream sources (e.g. BigQuery)
Data transformation
Business logic, validation, and testing are handled outside Power BI to keep the semantic layer lean and predictable.
Transformations and data quality checks are implemented using KNIME workflows.
- Data cleansing and normalization
- Business rule application
- Data validation and testing
- Analytics-ready datasets produced upstream
Loading and semantic modeling
Transformed datasets are loaded into Power BI and managed centrally via XMLA.
Dataset design focuses on performance, partitioning strategy, and controlled processing behavior.
- Dataset management via XMLA using SQL Server Management Studio
- Partitioned tables
- Selective and incremental partition processing
Reporting layer
Reports are built on top of a centralized semantic model to ensure consistency and reuse.
Visual design and interactions are optimized for performance and usability.
- Centralized semantic model
- Performance-aware report design
- Reusable datasets across reports
What this enables
By implementing a full end-to-end pipeline with clear separation of responsibilities, analytics delivery becomes predictable and easier to operate.
- Clear separation between ingestion, transformation, modeling, and reporting
- Controlled data processing for large datasets
- Scalable integration of additional external data sources
- Consistent business reporting on top of a single semantic layer