// 01
Six systems. Six different answers.
Data lives in separate applications and spreadsheets. Everyone exports their own version before they can answer a simple business question.
// 02 / Data Warehouse, ETL/ELT & Modelling
Your data shouldn't need a person to move it.
Disconnected systems. Fragile pipelines. Spreadsheets that need fixing every Monday. We build the data foundations that make reporting reliable without the constant manual work.
// No sales pitch. No commitment.
// From disconnected to dependable
Chaos in. Structured data out.
The work isn't just moving data from A to B. It's making sure the data arrives, gets checked, follows consistent definitions and is ready for the people who depend on it.
DATA PLATFORM / REFERENCE ARCHITECTURE
● VALIDATION REQUIRED
01 / SOURCES
Disconnected data
ERP systems
CRM platforms
SQL databases
Excel files
APIs
02 / INGEST
Reliable pipelines
Scheduled loads
Incremental updates
Error handling
Monitoring
Data checks
03 / MODEL
Trusted foundation
Bronze / Silver / Gold
Data cleaning
Business rules
Fact tables
Dimensions
04 / USE
Data ready to use
Power BI
Semantic models
Operational reports
Analytics
Downstream systems
PLATFORMS Microsoft Fabric
Azure Data Factory
Databricks
SQL
// Illustrative architecture. The right design depends on your existing systems, data volumes, requirements and budget.
// What brings people here
Does any of this sound familiar?
Most data engineering problems don't announce themselves as architecture problems. They show up as wasted hours, unreliable reporting and people doing work that systems should handle.
// 02
The pipeline failed. Again.
Refreshes break, columns change and scheduled jobs fail without anyone noticing until a dashboard shows yesterday's numbers.
// 03
Someone is the integration.
A person downloads files, combines worksheets and corrects data by hand. The process works only while that person is available.
// What we actually build
The unglamorous infrastructure that makes everything else work.
Not every business needs a new data platform. We work with what makes sense for your systems, reporting requirements and team.
01 / Integration
ETL & ELT pipelines
Move data between systems with appropriate scheduling, transformations, error handling and monitoring.
02 / Architecture
Data warehouses & lakehouses
Design structured storage that supports reporting, analytics and future changes without unnecessary complexity.
03 / Modelling
Dimensional models
Fact tables, dimensions and Kimball-style star schemas designed around the questions the business asks.
04 / Quality
Data validation
Checks for missing, duplicated or inconsistent records before bad data reaches business reports.
05 / Reliability
Monitoring & recovery
Make failures visible and design sensible retry, recovery and investigation processes.
06 / Maintainability
Documentation & handover
Clear pipeline logic, ownership and operational guidance so your team isn't dependent on guesswork.
// How we approach it
Assessment before architecture. Always.
The first question isn't whether you need Microsoft Fabric, Databricks or another platform.
It's what isn't working today, what the business needs from the data and which parts of the existing setup are worth keeping.
Sometimes that means a new warehouse. Sometimes it means fixing three unreliable pipelines and leaving the rest alone.
The objective is a dependable system, not the largest possible implementation.
01
Understand the current state
Review sources, pipelines, reporting dependencies, known failures and existing platform choices.
02
Identify the actual bottleneck
Separate problems with data quality, integration, modelling, performance and operational ownership.
03
Design the smallest sensible solution
Choose an architecture based on requirements, maintainability and cost rather than platform hype.
04
Build, validate and hand over
Implement the agreed changes, test the data and document what the team needs to operate it.
// Technology where it makes sense
Fabric, Databricks, SQL. Tools, not the starting point.
We work across Microsoft Fabric, Azure Data Factory, Databricks and SQL-based architectures. The right choice depends on what you already run and what you need to deliver.
// Fix the foundation
Still moving data by hand?
Tell us where the process breaks down. We'll help you understand whether you need a better pipeline, a cleaner model, a new foundation or simply a repair to what's already there.
Book a free call ↗
// No sales pitch. No commitment.