← Back to glossary

Data Engineering

The Data Engineering is the discipline of designing, building, and maintaining infrastructures that enable the collection, transformation, and exploitation of large quantities of data.
It forms the essential foundation for any serious approach toanalytics, reporting, or artificial intelligence, by ensuring reliable, available, and appropriately granular data.

Related Services

Data Engineering

Related Terms

ETL (Extract, Transform, Load) Scraping XML Feeds Machine learning

Data engineering in practice

Why data engineering is the foundation of data

  • Without reliable pipelines, no analytics, no AI, no reporting worthy of the name.
  • Centralises data scattered across the IS.
  • Ensures data quality, freshness, and availability.

Modern data stack

  • Ingestion - Airbyte, Fivetran, custom scripts.
  • Storage - data warehouse (Snowflake, BigQuery, Redshift) or data lake (S3, Iceberg).
  • Transformation - dbt for models, Spark for large volumes.
  • Orchestration - Airflow, Dagster, Prefect.
  • Visualisation - Metabase, Looker, Power BI.

Caveats

  • GDPR - minimisation of personal data, anonymisation.
  • Cloud costs (compute + storage) - monitor from day one.
  • Documentation and lineage - otherwise no one knows where a figure comes from.

How Galadrim can support you

See our offering Data Engineering.

[Contact us]

Let's bring your project to life together

We work with all types of clients, across all sectors. Whether you're an entrepreneur or managing a large organisation, a tailored team will meet your needs.

Over 800 companies have trusted us to create their web, mobile, and AI products

Your enquiry

We'll get back to you within 1 hour.