Data & AI / Data engineering
The foundation
that holds it all up.
Pipelines, a data warehouse and reliable data. We build the infrastructure that captures, cleans and organises your company’s information, so that your business intelligence, your predictive analytics and your AI rest on real data — not on scattered spreadsheets.
What it is
The invisible foundation
under every decision.
Data engineering is the discipline that captures, moves, cleans, models and serves a company’s data so that it is ready to use. It is the plumbing no one sees: the pipes through which information travels from where it is generated —your CRM, your ERP, your website, your sensors— to where it is consumed: a dashboard, a predictive model, a report.
Without this layer, every analysis starts from scratch: someone exports a spreadsheet by hand, combines it with another, fixes errors and produces a figure no one else can reproduce. Data engineering turns that chaos into a reliable, repeatable system. It is the foundation on which everything else is built —business intelligence, predictive analytics and artificial intelligence— which is why it is almost always the first step, not the last. It is part of ourdata and AI practice.
Problems it solves
What happens when data
is left uncared for.
Data in silos
Each department keeps its own truth in its own tool. Sales, finance and operations do not share a single figure, and combining them is a manual project every time.
Dirty and duplicate data
The same customer shows up three times with different names. Fields are missing, formats are inconsistent, and no one fully trusts the final number.
No single source of truth
Two reports give two numbers for the same question. The debate is about which figure is right, instead of about which decision to make.
Manual data work
Someone spends hours every week exporting, pasting and cleaning spreadsheets. It is slow, tedious and a factory of errors that are hard to trace.
No history
Systems only store the current state. When you want to analyse trends over time or train a model, you find that the data is already gone.
It does not scale
What worked with a spreadsheet breaks as volume grows. Queries slow down, processes fail and the business waits.
What we build
The data platform,
piece by piece.
We build the full infrastructure so that data reaches whoever needs it — clean, organised and on time:
- ETL/ELT data pipelines
- Cloud data warehouse or lakehouse
- Data modelling (layers and schemas)
- Data quality (tests and validation)
- Data catalogue and governance
- Source integration (CRM, ERP, APIs, files)
- Orchestration and scheduling
- Documentation and data lineage
Cases by sector and area
The same foundation,
in every sector.
| Sector or area | Data challenge | Result |
|---|---|---|
| Retail / e-commerce | Sales, stock and web in separate systems | A single warehouse with real, up-to-date sales, margin and stock. |
| Manufacturing | Machine data and ERP not connected | A production history ready for predictive maintenance and analysis. |
| Finance / Insurance | Manual reconciliation across many sources | Reconciled, auditable data with no manual balancing every close. |
| Healthcare | Scattered records and sensitive data | Unified data with access control and full traceability. |
| SaaS / Technology | Product events with no common model | Reliable product metrics, ready for BI and for models. |
The core idea
Without reliable data, the best AI only speeds up the errors.
Benefits and results
What you gain with
data in order.
A single source of truth
One place where the data is correct. No more arguments about which number is right.
Reliable, timely data
Validated, up-to-date information available when it is needed — not two days later.
A solid foundation for AI
Predictive models and AI trained on clean, complete data, not on garbage.
Less manual work
Manual exports, joins and clean-ups are replaced by automated, repeatable processes.
Scalability
A platform that grows with you: from the first gigabyte to millions of records without rebuilding it all.
Cost under control
Cloud architectures sized and optimised so you pay for what you use, not for more.
Stack and approach
Proven tools,
chosen with judgment.
We do not marry a single technology. We choose each piece for robustness, cost and maintainability, favouring open standards so you do not depend on one vendor.
The CPPA method
From data chaos to a
reliable platform.
Source and data audit
We map where your data comes from, what state it is in and which business questions it must answer. Without a diagnosis there is no architecture worth building.
Architecture design
We define the data model, the platform (warehouse or lakehouse) and how information flows. We choose based on real need, not on technology fashion.
Building the pipelines
We connect the sources, build the ETL/ELT processes with tests and validation, and set up the warehouse. Every piece of data leaves a trail (lineage) and every process alerts if it fails.
Go-live and governance
We publish data ready for BI and AI, document the catalogue, define permissions and leave monitoring in place. The platform is documented and ready to grow.
Pipeline examples
What it looks like
in practice.
Unify sources into a warehouse
- Connectors to CRM, ERP and web
- Incremental data extraction
- Load into the data warehouse
- Transformation and modelling with dbt
- Data ready for BI
Real-time data pipeline
- Events published to Kafka
- Streaming ingestion
- Validation and enrichment
- Write to the warehouse
- Dashboard updated by the second
Data quality layer
- Rules and tests per table
- Validation on every load
- Alert when data fails a check
- Quarantine for faulty records
- Quality report by source
Data model for BI
- Raw sources
- Cleaning and standardisation
- Dimensional model (facts and dimensions)
- Business metrics defined once
- Consumed by dashboards and models
Risks and mitigation
Where it fails,
and how we prevent it.
A poorly designed data platform creates more problems than it solves. These are the risks we watch from day one:
Data quality
A warehouse full of bad data is worse than none at all. We prevent it with automated tests, validation on every load and quarantine for anything that fails.
Governance and access
Without control, a data lake turns into a swamp with no owner. We define a catalogue, role-based permissions and lineage so you always know where each figure comes from.
Runaway cloud cost
The cloud makes it easy to overspend. We size the platform, optimise queries and storage and set spend alerts so cost stays predictable.
Unnecessary complexity
It is tempting to build a huge architecture “just in case”. We start with the simplest thing that solves today’s problem and grow only when needed.
Vendor lock-in
We favour open standards (SQL, formats like Parquet) and portable tools, so the platform does not tie you to a single vendor.
Sensitive data and GDPR
We handle personal data with least privilege, encryption and anonymisation where appropriate, in line with the GDPR and with auditable logs.
Frequently asked questions
What is a data warehouse?
What is the difference between ETL and ELT?
Do I need data engineering before doing AI?
Cloud or on-premise?
How do you guarantee data quality?
How long does it take?
Related services
Keep exploring.
Business intelligence & dashboards
Once the data is reliable, dashboards turn it into clear, real-time decisions.
View →
Machine learning
With clean data and a solid history, models learn from your business: they classify, predict and detect.
View →
Artificial intelligence
Data engineering is the foundation; AI is what you build on top, with judgment and human control.
View →Is your data a foundation, or a problem?
Request a proposal →Want to see how we read a real business through its data? Read ourCPPA X-RAY on 100 Montaditos.