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CourseDefense, Military Engineering & Land Systems Applications open

ISO/IEC Course in Critical Data Integration

Online 300-hour ISO/IEC course in critical data integration. 6 modules covering ISO/IEC 25012 (data quality model), ISO 8000-61 (data quality management) and Apache Kafka. Assigned tutor and enrolment open.

Duration300 h
ModeOnline
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Identify data integration requirements in critical systems.

02

Model data and metadata with shared semantics.

03

Measure data quality using standardised indicators.

04

Design batch and real-time integration flows.

05

Apply security and compliance controls to integrated data.

06

Plan the operation and governance of data.

07

Develop a justified integration architecture.

Profile

Who it's for

  • Data engineer wishing to specialise in ISO/IEC for critical data integration.
  • Data architect wishing to specialise in ISO/IEC for critical data integration.
  • Data quality manager wishing to specialise in ISO/IEC for critical data integration.
  • Systems integration engineer wishing to specialise in ISO/IEC for critical data integration.
  • Database administrator wishing to specialise in ISO/IEC for critical data integration.
  • Data governance manager wishing to specialise in ISO/IEC for critical data integration.
  • Graduates in engineering or sciences seeking to enter the defence industry.
Standards & tools

What you'll work with

  • ISO/IEC 25012 (data quality model)
  • ISO 8000-61 (data quality management)
  • ISO/IEC 11179-3 (metadata registries)
  • ISO/IEC 25024 (measurement of data quality)
  • ISO 15926-2 (lifecycle data integration)
  • OMG DDS (data distribution service)
  • ISO/IEC 27001 (information security management)
Syllabus

6 modules · 300 h

  1. 1.1Why integrate: silos, duplication and slow decisions
  2. 1.2Critical data and its definition within an organisation
  3. 1.3Integration patterns: batch, event-driven and service-based
  4. 1.4Data architectures: warehouse, lake and mesh
  5. 1.5Data governance and responsibilities
  6. 1.6International standards for data quality and management
  7. 1.7Field vocabulary and acronyms
  8. 1.8Stakeholders and their relationships
  9. 1.9Failure cases and their causes
  10. 1.10Exercise: mapping the data flows of a process
  1. 2.1Conceptual, logical and physical models
  2. 2.2Data dictionary and controlled vocabularies
  3. 2.3Ontologies and taxonomies
  4. 2.4Unique identifiers and their management
  5. 2.5Master data and reference data
  6. 2.6Schema mappings
  7. 2.7Semantic interoperability between organisations
  8. 2.8Model versioning and compatibility
  9. 2.9Model documentation
  10. 2.10Practice: defining the semantic model of a domain
  1. 3.1Quality dimensions: accuracy, completeness and timeliness
  2. 3.2Metrics and acceptance thresholds
  3. 3.3Data profiling and anomaly detection
  4. 3.4Validation rules and their automation
  5. 3.5Cleansing, normalisation and deduplication
  6. 3.6Entity resolution and matching
  7. 3.7Data lineage and traceability
  8. 3.8Data quality incident management
  9. 3.9Quality indicators and dashboard
  10. 3.10Practice: defining quality rules for a dataset
  1. 4.1Application programming interfaces and their governance
  2. 4.2Messaging, queues and publish/subscribe
  3. 4.3Stream processing and time windows
  4. 4.4Synchronisation and timestamps
  5. 4.5Idempotency and guaranteed delivery
  6. 4.6Error handling and retries
  7. 4.7Latency and service level agreements
  8. 4.8Integration with legacy systems
  9. 4.9Integration testing and environments
  10. 4.10Practice: designing an event-driven integration
  1. 5.1Information classification and labelling
  2. 5.2Data-level access control
  3. 5.3Encryption in transit and at rest
  4. 5.4Pseudonymisation and minimisation
  5. 5.5Data protection and lawful bases
  6. 5.6Retention, archiving and deletion
  7. 5.7Auditing of access and changes
  8. 5.8Data continuity and recovery
  9. 5.9Data catalogue and responsible self-service
  10. 5.10Practice: governance plan for a critical dataset
  1. 6.1Choosing the process and the systems involved
  2. 6.2Flow mapping and diagnosis
  3. 6.3Semantic model and master data
  4. 6.4Data quality rules and plan
  5. 6.5Technical integration design
  6. 6.6Security and compliance plan
  7. 6.7Catalogue, lineage and indicators
  8. 6.8Phased implementation plan
  9. 6.9Technical report
  10. 6.10Project defence
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, which makes it possible to combine with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Apache Kafka, Apache NiFi, Talend, PostgreSQL, Python (pandas), in free, academic or evaluation versions. Tests are studied using real procedures for integrating heterogeneous sources in the laboratory, data quality profiling and auditing, and latency testing on real-time flows. Assessment is continuous by module, plus the final integrative project.

Accredited laboratories(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-oriented Master's Thesis:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:faculty with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, ciber-OT, DIH and compliance as its pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Choosing the process and the systems involved

Identify data integration requirements in critical systems.

Flow mapping and diagnosis

Model data and metadata with shared semantics.

Semantic model and master data

Measure data quality using standardised indicators.

Data quality rules and plan

Design batch and real-time integration flows.

Technical integration design

Apply security and compliance controls to integrated data.

Career opportunities

Where this takes you

Data engineerData architectData quality managerSystems integration engineerDatabase administratorData governance manager
Admissions & scholarships

Requirements

  • No prior degree required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient Spanish to follow technical standards documentation.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 300 hours spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.

You work with Apache Kafka, Apache NiFi, Talend, PostgreSQL, Python (pandas). Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

A SEIUM Institute diploma upon passing the continuous assessment and the integrative project. It is in-house training, not an official qualification from the Ministry.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the advanced programme. Check the recognition policy when you enrol.

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