The Challenge
The problem with traditional ETL.
Data pipelines shouldn't require a dedicated engineering team just to keep them running. When every new source is a custom script, your architecture becomes a liability.
The Spaghetti Architecture
Your systems are connected point-to-point. The CRM talks directly to the billing system via a script someone wrote three years ago. The ERP exports to a shared folder. When one connection breaks, troubleshooting takes days because there is no central orchestration.
Pipelines that Break on Minor Changes
Someone in sales added a new custom field in Salesforce. The next morning, the financial reporting pipeline fails because the hardcoded schema expected 42 columns, not 43. Your data engineering team spends hours adjusting code for tiny operational changes.
Data Quality is an Afterthought
You ingest data successfully, but the data itself is useless. Null values where primary keys should be, negative revenue amounts, dates in the wrong format. You don't know the data is bad until a business user points it out in a dashboard.
Deliverables
A modern approach to ingestion.
Metadata-Driven Framework
A central control table in Fabric that drives ingestion. Want to add a new table from the ERP? You add a row to the configuration table. You don't write new Data Factory pipeline code.
API & System Accelerators
Pre-built integration patterns for common systems (Salesforce, SAP, HubSpot) that handle API pagination, token refreshes, and rate limiting smoothly.
Schema Drift Handling
Engineering that automatically detects when source columns change, ensuring pipelines continue to run while intelligently alerting administrators.
Automated Quality Gates & Orchestration
Validation checks run during processing. Bad records are quarantined without failing the entire batch. Centralized scheduling using Fabric Data Factory notifies teams exactly why a failure occurred.
Methodology
How we build pipelines
Profiling, architecture, development, and operational handoff.
Source System Profiling
We analyze the source applications—APIs, databases, flat files—to understand access methods, volume, velocity, and data quality constraints before designing the connection.
Architecture Design
We design the specific metadata framework and incremental loading strategy (CDC, watermark columns) tailored to Microsoft Fabric's Lakehouse capabilities.
Pipeline Development
We build the ingestion engine, configuring Data Factory, PySpark Notebooks, and security credentials mapping out Azure Key Vault implementations.
Testing & Operational Handoff
We conduct stress testing, implement error-handling runbooks, and train your team on how to manage and extend the dynamic metadata framework.
Orchestrating data for a private equity healthcare roll-up.
The Situation
A PE firm acquired eight healthcare clinics in 18 months. Each clinic used a different EMR system and accounting software. The central analytics team was spending 60 hours a month manually downloading reports and manipulating Excel files, resulting in broken data transfers and zero operational visibility.
The Solution
- Implemented a metadata-driven ingestion framework on Microsoft Fabric.
- Standardized API connectors for the three most common EMR systems.
- Built automated data quality gates that quarantine bad patient records.