Master’s in Data, Real-Time and Platforms Engineering
Online master's programme of 1,900 hours in data engineering, real-time processing and platforms: 12 modules covering modern data architectures, batch and streaming processing, orchestration, data quality and governance, cloud platforms, observability, security and data for AI, with an assigned technical tutor.
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What you will be able to do
Design batch and real-time data architectures.
Model analytical data using dimensional and Data Vault approaches.
Build distributed pipelines with Spark, Kafka and Flink.
Orchestrate and test data transformations reproducibly.
Apply data quality, lineage and governance policies.
Deploy cloud data platforms with infrastructure as code.
Ensure the reliability and security of data platforms.
Who it's for
- Data engineers looking to master real-time processing and platform design.
- Data architects designing lakehouse and streaming architectures.
- Data platform engineers building self-service tooling for domain teams.
- Streaming engineers working with Kafka, Flink and CDC pipelines.
- Data governance leads responsible for quality, lineage and compliance.
- Data reliability engineers in charge of SLOs, monitoring and incidents.
- Engineering or science graduates aiming to move into banking and insurance.
What you'll work with
- Regulation (EU) 2016/679 (GDPR) (EU General Data Protection Regulation)
- ISO/IEC 27001 (information security management systems)
- ISO 8000-8 (data quality: information and data quality concepts)
- Regulation (EU) 2023/2854 (Data Act) (European rules on fair access to and use of data)
- ISO/IEC 25012 (data quality model)
- ISO/IEC 20547-3 (big data reference architecture)
- ISO/IEC 38505-1 (governance of data)
- Apache Spark
- Apache Kafka
- Apache Airflow
- dbt
- Apache Flink
12 modules · 1900 h
1.1From the data warehouse to the lakehouse1.2Lambda and Kappa architectures1.3Data mesh and data domains1.4OLTP versus OLAP models1.5Columnar storage and open formats1.6Separation of compute and storage1.7Batch and streaming ingestion patterns1.8Designing bronze, silver and gold layers1.9Technology selection criteria1.10Case study: data architecture for an industrial company
2.1Kimball dimensional modelling2.2Data Vault 2.02.3Fact tables and slowly changing dimensions2.4Parquet and ORC formats2.5Open tables with Delta Lake, Iceberg and Hudi2.6Partitioning, clustering and compaction2.7Time-series databases2.8Key-value and document NoSQL stores2.9Vector databases2.10Lab: dimensional model in a lakehouse
3.1Apache Spark execution model3.2DataFrames, logical and physical plans3.3Partitions, shuffles and skew3.4Optimisation with caching and broadcast3.5Spark SQL and window functions3.6Incremental processing and data changes3.7Distributed SQL engines such as Trino3.8Cluster cost and sizing3.9Job performance testing3.10Practical: optimising a Spark job with skew
4.1Distributed logs and Apache Kafka4.2Partitions, replicas and delivery guarantees4.3Schemas and Schema Registry4.4Change data capture with Debezium4.5Processing with Apache Flink4.6Event time, watermarks and windows4.7State and checkpoints4.8Exactly-once semantics4.9Kafka Connect and connectors4.10Lab: CDC pipeline from a database to Kafka
5.1Dependency graphs and DAGs5.2Apache Airflow: operators and sensors5.3Dagster and asset-based orchestration5.4Transformations with dbt5.5Testing and documentation in dbt5.6Managing retries and backfills5.7Idempotency in pipelines5.8Parameterisation and environments5.9ELT versus ETL patterns5.10Workshop: orchestrated pipeline with Airflow and dbt
6.1Data quality dimensions according to ISO 80006.2Data contracts between producers and consumers6.3Validations with Great Expectations6.4Data lineage with OpenLineage6.5Catalogues and metadata6.6Master data management6.7Classification and sensitive data6.8GDPR compliance in platforms6.9Retention policies6.10Practical: data contract with automated validations
7.1Managed services on AWS, Azure and GCP7.2Snowflake and BigQuery7.3Databricks and the lakehouse platform7.4Networking, identities and permissions7.5Infrastructure as code with Terraform7.6Data costs and FinOps7.7Multi-cloud and portability7.8On-premise platforms and Kubernetes7.9Disaster recovery7.10Lab: deploying a lakehouse with Terraform
8.1The data platform as an internal product8.2Self-service for domain teams8.3Templates and golden paths8.4CI/CD for data pipelines8.5Containers and Kubernetes for data8.6Secrets and configuration management8.7Ephemeral test environments8.8Data developer experience8.9DORA metrics applied to data8.10Workshop: CI/CD for a dbt project
9.1Freshness, volume and schema metrics9.2Data SLAs, SLOs and SLIs9.3Monitoring with Prometheus and Grafana9.4Alerting and incident management9.5Anomaly detection in data9.6Root cause analysis with lineage9.7Streaming reliability and consumer lag9.8Runbooks and on-call rotas9.9Blameless postmortems9.10Practical: freshness SLO for a critical pipeline
10.1Role-based and attribute-based access control10.2Encryption at rest and in transit10.3Masking and tokenisation10.4Access auditing10.5Security according to ISO/IEC 2700110.6Privacy by design10.7International data transfers10.8The European Data Act10.9Third-party risk management10.10Case study: permissions model in a lakehouse
11.1Feature stores and feature reuse11.2Serving data to models in real time11.3Semantic layer and metrics11.4Data for self-service BI11.5Retrieval-augmented generation with vector databases11.6Unstructured data and documents11.7Data APIs and services11.8Data monetisation and sharing11.9Architectures for operational analytics11.10Workshop: feature store with streaming data
12.1Defining the data use case12.2Functional and non-functional requirements12.3Designing the target architecture12.4Implementing ingestion and processing12.5Quality, governance and security12.6Observability and testing12.7Performance and cost evaluation12.8Conclusions and evolution12.9Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
100% online training on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Apache Spark, Apache Kafka, Apache Airflow, dbt and Apache Flink, in free, academic or evaluation versions. Real procedures are studied, such as designing a lakehouse with streaming ingestion, a CDC pipeline with exactly-once guarantees and migrating a data warehouse to the cloud. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: Regulation (EU) 2016/679 (GDPR), ISO/IEC 27001, ISO 8000-8, Regulation (EU) 2023/2854 (Data Act), ISO/IEC 25012, ISO/IEC 20547-3, ISO/IEC 38505-1.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- Internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with Apache Spark, Apache Kafka, Apache Airflow, dbt and Apache Flink, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.
A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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