Capability
Data Engineering
Pipelines, warehouses, lakehouses, big-data processing and AI-ready data preparation. Ingestion, modelling, lineage and governance are designed together, so trust in the data grows with its volume.
Reliable data foundations — ingestion, modelling, lineage and governance — that AI and analytics products can depend on.
01Architecture
Reference architecture
A typical arrangement of the components. Every implementation is adapted to your systems, data-residency and security requirements.
Sources
- ERP, CRM and operational databases
- Files and external data
- Event streamsKafka
Pipelines
- Batch and streaming ELTAirflow · Spark
- Quality checks and contractsdbt
Storage
- WarehouseSnowflake · BigQuery · Postgres
- LakehouseIceberg
Consumers
- Dashboards and reports
- AI and machine-learning features
02Components
What this capability covers
01
Pipelines and ELT
Batch and streaming ingestion with retries, data contracts and observability built in.
02
Warehouse modelling
Dimensional and event-driven models that stay aligned with business definitions.
03
Data lakes and lakehouse
Open-format storage for structured, unstructured and machine-learning feature data.
04
Quality and lineage
Automated checks, documentation and lineage, so trust in the data keeps pace with its volume.
03Implementation
How we implement it
We model data around business definitions agreed with the people who use them, and make those definitions visible in documentation and tests. Data residency and access requirements are designed into storage and pipelines from the start.
- Data contracts define what each source delivers.
- Quality checks run inside the pipeline and stop bad data before it spreads.
- Lineage shows where every figure comes from.
04Deliverables
What you receive
Data-source inventory and contracts
Pipelines with quality checks
Documented data models
Lineage and access controls
Technology
Technologies we work with
- Airflow
- dbt
- Spark
- Kafka
- Snowflake
- BigQuery
- Postgres
- Iceberg
Chosen per project and aligned with your existing standards. Listed as technologies we use, not as partnerships or endorsements.
05Use cases
Typical applications
Patterns this capability is designed for, not a list of delivered projects.
01
AI-ready feature stores
Curated, versioned features that feed machine learning and analytics in production.
02
Event analytics platform
Streaming pipelines that feed real-time dashboards and downstream models.
03
Legacy modernisation
Fragile ETL jobs migrated into observable, modular pipelines.
06Evidence
Related work
Projects and programmes in which this capability played a part, described with the role NLAI actually had.
- Collaborative research
Vision for Robotfusion
NLAI takes part in Vision for Robotfusion, a collaborative project in the Brabant AI community whose partners include Brabant.ai, Brainport Development and Breda Robotics.
Read more
07Related
Where to go next
Industry contexts
More in Digital Platforms & Products
- Analytics & Dashboards
Interactive BI dashboards, KPI tracking, real-time monitoring and behaviour analytics built on trusted data.
- AI-Powered Platforms
AI-enhanced websites, SaaS platforms, smart web and mobile apps and personalisation engines, with search, recommendations and assistants designed into the experience.
Next step
Scope Data Engineering for your organisation
Share your process, data and constraints. We will propose an approach, the evidence to collect first and a realistic plan.