Fabric Lakehouse Data Engineering— built for scale and speed.
Develop high-performance data pipelines using PySpark, SQL, and Delta Lake. Transform raw data into AI-ready assets within Microsoft Fabric's Medallion architecture.
"Stop choosing between the flexibility of a data lake and the reliability of a data warehouse. We build optimized Lakehouse architectures on Microsoft Fabric — giving your AI and analytics teams a unified, high-performance foundation built on open Delta formats."
Sound familiar?
- Data swamps with poor quality and zero governance
- Slow, brittle ETL pipelines that constantly fail or bottleneck
- High costs from maintaining separate data lakes and data warehouses
- Difficulty handling streaming and batch data in the same architecture
- Data engineers spending 80% of their time on infrastructure, not logic
- Incompatible data formats slowing down AI and data science initiatives
What's included
- Lakehouse architecture design (Medallion pattern)
- Custom Apache Spark & Python pipeline development
- Delta Lake table optimization (V-Order, Compaction)
- Real-time streaming and batch data ingestion
- Fabric Notebooks & automated job scheduling
- Data quality enforcement & error handling routines
- CI/CD deployment for data engineering artifacts
- Performance tuning for large-scale datasets
National Retail Chain Data Platform
A major retail brand was struggling with 8-hour overnight batch jobs failing frequently. We rebuilt their pipelines using Fabric Notebooks and PySpark inside a Medallion architecture, handling multi-terabyte inventory logs efficiently.
Who this is for
Core Industries
Target Buyers
Delivery Timeline
Depending on pipeline complexity and data volume
Ready to engineer your data for the future?
Book a 30-minute conversation. We'll review your current data pipelines, discuss your Spark and SQL engineering needs, and outline what a Fabric Lakehouse engagement looks like — at no cost.