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EYA-F03Continuous training programme

Data Governance & Data Engineering

Build a compliant, operational data strategy

Duration
21 hours (3 days)
Quote response lead time
24 working hours
Training start lead time
First start available 7 to 30 days after signing the agreement (subject to scheduling). Agreement signed ≥ 7 working days before D1.
Format
Blended Learning (50% in-person + 50% online)
Group size
12 participants maximum
Location
On-site, in-person Paris or online
Language
French (English on request)

Access conditions

  • ▸Personalised quote within 24 working hours
  • ▸Training agreement signed by both parties
  • ▸Prior positioning test (≥ D-5)
  • ▸Preparatory documents (programme, welcome booklet, internal rules) provided before start

Lead times to training

  • ▸Agreement signed ≥ 7 working days before training start
  • ▸First D1 available 7 to 30 days after signing the agreement (subject to scheduling and funding)
  • ▸Average cumulative lead time: 14 to 30 days between first request and training start

Evaluation methods

  • ▸Entry positioning test (self-assessment + quiz, ≥ D-5)
  • ▸Formative quizzes during session (D1, D2, D3 depending on duration)
  • ▸Individually graded deliverable (validation threshold 65/100)
  • ▸End-of-session evaluation questionnaire (D0)
  • ▸Follow-up evaluation questionnaire (D+90 — field impact)
  • ▸Attendance certificate and realisation certificate provided at end

Overview

From data governance (GDPR, quality, roles) to modern data architecture (Lakehouse, pipelines, DataViz), a pragmatic training to drive your data strategy.

Learning objectives

  • Define and implement a data governance policy
  • Master Data Engineering concepts: pipelines, Data Lake, Data Warehouse
  • Apply regulatory frameworks (GDPR, data quality)
  • Select and evaluate modern data stack tools

Detailed programme

01

Data governance: stakes and frameworks

3h

Definition. Roles: Data Owner, Data Steward, CDO. DAMA-DMBOK framework. Data quality: completeness, accuracy, consistency, freshness. GDPR and compliance.

02

Modern Data Architecture

4h

Data Lake, Data Warehouse, Data Lakehouse. Lambda and Kappa architectures. Ingestion: ETL vs ELT. Data catalogues. Modern stack: Spark, Kafka, dbt, Airflow.

03

Hands-on Data Engineering

4h

Building a data pipeline (demo). Dimensional modelling (Kimball). DataViz: Power BI, Tableau, Metabase. AI/ML on enterprise data. Data architecture workshop.

04

Implementation & data strategy steering

3h

Data strategy and roadmap. Data maturity KPIs. Organisation: team structure, DataOps. Data ROI. Sector feedback (insurance, public sector, logistics).

Evaluation & follow-up

Initial quiz (data maturity). Pair data architecture design exercise. Post-session evaluation. Individual attendance certificate.

Pedagogical resources

Real cases from data projects (public sector, banking, World Bank). Live demos (Power BI, Python pipeline). Sandbox provided if needed.

Trainer qualification

Expert in Data Engineering and Governance. Hortonworks Big Data certified (2015). Hands-on experience with large-scale data.

Page last updated on : 08/05/2026