Apply now
Master'sData, AI, Simulation & Analytics Applications open

Master’s in Real-Time Analytics and Stream Processing

Online master's programme of 1,900 hours in real-time analytics and stream processing: Kafka/Pulsar, CEP, operational KPIs and low latency. 12 modules covering data in motion, distributed messaging, ingestion from the physical world, stream processing, streaming engines, real-time storage, real-time analytics and machine learning, visualisation and alerting, operations, security and governance, and industrial applications, with an assigned technical tutor.

Duration12–14 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
Enrol now View syllabus Ask an advisor
Virtual lab · try it before you enrol

Take the controls.

Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.

SIM · LIVE ●
Learning outcomes

What you will be able to do

01

Design architectures for data in motion.

02

Operate distributed messaging systems.

03

Ingest data from sensors and industrial systems.

04

Process streams with event-time semantics.

05

Apply real-time analytics and machine learning.

06

Operate scalable, reliable pipelines.

07

Govern and secure streaming platforms.

Profile

Who it's for

  • Real-time data engineers.
  • Streaming platform architects.
  • Data reliability engineers.
  • Time-series data scientists.
  • IoT application developers.
  • Data platform managers.
  • Engineering or science graduates looking to enter Industry 4.0.
Standards & tools

What you'll work with

  • ISO/IEC 20922 (MQTT messaging protocol)
  • ISO/IEC 19464 (AMQP messaging protocol)
  • IEC 62541 (OPC UA)
  • ISO/IEC 9075 (SQL database language)
  • ISO/IEC 25012 (data quality model)
  • ISO/IEC 27001 (information security management)
  • Regulation (EU) 2016/679 (General Data Protection Regulation)
  • Apache Kafka
  • Apache Flink
  • Spark Structured Streaming
  • ClickHouse
  • Grafana
Syllabus

12 modules

  1. 1.1Data at rest versus data in motion
  2. 1.2Real-time use cases
  3. 1.3Latency, throughput and freshness
  4. 1.4Lambda and kappa architectures
  5. 1.5Events, messages and logs
  6. 1.6Delivery semantics
  7. 1.7Non-functional requirements
  8. 1.8Technology ecosystem
  9. 1.9Costs and sizing
  10. 1.10Case study: requirements for a telemetry platform
  1. 2.1Queues versus distributed logs
  2. 2.2Apache Kafka architecture
  3. 2.3Topics, partitions and replicas
  4. 2.4Producers and consumers
  5. 2.5Consumer groups
  6. 2.6Schemas and Schema Registry
  7. 2.7Kafka Connect
  8. 2.8Retention and compaction
  9. 2.9Alternatives: Pulsar and RabbitMQ
  10. 2.10Lab: Kafka cluster with Docker
  1. 3.1MQTT and AMQP protocols
  2. 3.2OPC UA in industry
  3. 3.3IoT gateways
  4. 3.4Sensor data and time series
  5. 3.5Change data capture from databases
  6. 3.6Data quality at source
  7. 3.7Clock synchronisation
  8. 3.8Compression and binary formats
  9. 3.9Security in ingestion
  10. 3.10Practical exercise: MQTT ingestion into Kafka
  1. 4.1Dataflow model
  2. 4.2Event time and processing time
  3. 4.3Watermarks
  4. 4.4Time windows
  5. 4.5State and checkpoints
  6. 4.6Exactly-once processing
  7. 4.7Stream joins
  8. 4.8Complex stateful processing
  9. 4.9Reprocessing historical data
  10. 4.10Lab: time windows with Apache Flink
  1. 5.1Apache Flink in depth
  2. 5.2Spark Structured Streaming
  3. 5.3Kafka Streams
  4. 5.4SQL on streams
  5. 5.5ksqlDB and Flink SQL
  6. 5.6Serverless processing
  7. 5.7Managed cloud services
  8. 5.8Engine comparison
  9. 5.9Technology selection
  10. 5.10Practical exercise: continuous query with Flink SQL
  1. 6.1Time-series databases
  2. 6.2Real-time OLAP databases
  3. 6.3Apache Druid and ClickHouse
  4. 6.4Incremental materialised views
  5. 6.5Open table formats
  6. 6.6Streaming lakehouse architecture
  7. 6.7Caches and key-value stores
  8. 6.8Low-latency queries
  9. 6.9Life-cycle management
  10. 6.10Lab: real-time dashboard with ClickHouse
  1. 7.1Continuous aggregations and indicators
  2. 7.2Anomaly detection on streams
  3. 7.3Complex event processing
  4. 7.4Online machine learning
  5. 7.5Model inference in streaming
  6. 7.6Feature stores
  7. 7.7Data and concept drift
  8. 7.8Real-time forecasting
  9. 7.9Continuous model evaluation
  10. 7.10Practical exercise: anomaly detector for telemetry
  1. 8.1Real-time dashboards
  2. 8.2Grafana and streaming sources
  3. 8.3Designing useful alerts
  4. 8.4Alarm fatigue
  5. 8.5Notifications and escalation
  6. 8.6Time-series visualisation
  7. 8.7Real-time maps
  8. 8.8Operator interfaces
  9. 8.9User testing
  10. 8.10Workshop: alerting system for a sensor network
  1. 9.1Deployment on Kubernetes
  2. 9.2Scaling partitions and tasks
  3. 9.3Backpressure
  4. 9.4Fault tolerance
  5. 9.5Disaster recovery
  6. 9.6Pipeline observability
  7. 9.7Load testing
  8. 9.8Schema version management
  9. 9.9Operating costs
  10. 9.10Lab: load testing of a pipeline
  1. 10.1Authentication and authorisation
  2. 10.2Encryption in transit and at rest
  3. 10.3Multi-tenancy
  4. 10.4Personal data in streams
  5. 10.5Real-time anonymisation
  6. 10.6Data lineage and catalogue
  7. 10.7Data contracts
  8. 10.8Auditing and compliance
  9. 10.9Schema governance
  10. 10.10Case study: governance of an event platform
  1. 11.1Power grid monitoring
  2. 11.2Predictive maintenance in factories
  3. 11.3Fleet management and logistics
  4. 11.4Urban mobility and traffic
  5. 11.5Fraud detection
  6. 11.6Telecommunications
  7. 11.7Event-driven cybersecurity
  8. 11.8Real-time digital twins
  9. 11.9Return on investment
  10. 11.10Case study: telemetry platform for a train fleet
  1. 12.1Definition of the use case
  2. 12.2State of the art
  3. 12.3Requirements and architecture
  4. 12.4Ingestion and event modelling
  5. 12.5Stream processing
  6. 12.6Analytics and visualisation
  7. 12.7Performance testing
  8. 12.8Conclusions
  9. 12.9Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

The programme is delivered 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so it fits around your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Apache Kafka, Apache Flink, Spark Structured Streaming, ClickHouse and Grafana, in free, academic or evaluation versions. Exercises are based on real procedures: load testing of event pipelines, anomaly detection in telemetry and continuous queries on streams. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO/IEC 20922, ISO/IEC 19464, IEC 62541, ISO/IEC 9075, ISO/IEC 25012, ISO/IEC 27001, Regulation (EU) 2016/679.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO/IEC 20922, ISO/IEC 19464 and IEC 62541 applied to real cases, not as a theoretical annex.
Industry software:Apache Kafka, Apache Flink, Spark Structured Streaming, ClickHouse and Grafana, in free, academic or evaluation versions.
Master's Thesis with defence:a complete real-time analytics platform for a real use case, from ingestion to visualisation and performance testing, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

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

Kafka ingestion pipeline
Set up a Kafka cluster and ingest MQTT sensor data into it.
Windowed stream processing
Implement event-time windows and continuous queries with Apache Flink.
Real-time anomaly detection
Build an anomaly detector for telemetry with real-time dashboards and alerts.
Load and reliability testing
Load test the pipeline and tune its scalability and fault tolerance.
Master's Thesis
Deliver a complete streaming platform for a real use case, defended before a panel.
Career opportunities

Where this takes you

Real-time data engineerStreaming platform architectData reliability engineerTime-series data scientistIoT application developerData platform manager
Admissions & scholarships

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.

Apply now Scholarships
FAQ

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 Kafka, Apache Flink, Spark Structured Streaming, ClickHouse and Grafana, 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 suitable computer 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.

Keep exploring

Related programmes

See all in Data, AI, Simulation & Analytics
Master's9,970 $

Master’s in Digital Twins and Operations Analytics Engineering

Online master's programme of 1,900 hours in digital twin engineering and operations analytics. 12 modules covering digital twin architecture, data integration, physical, data-driven and hybrid modelling,…

1900 hOnlineData, AI, Simulation & Analytics
Master's8,850 $

Master’s in Traffic and Mobility Twins Engineering

Online master's programme of 1,900 hours in traffic and mobility digital twin engineering. 12 modules covering traffic engineering, traffic models, mobility data, real-time traffic twins, AI-based…

1900 hOnlineData, AI, Simulation & Analytics
Master's9,970 $

Master’s in Reinforcement Learning, Control and Safe AI Engineering

Online master's programme of 1,900 hours in reinforcement learning, control and safe AI engineering. 12 modules covering sequential decision-making, deep RL, model-based learning, classical and modern…

1900 hOnlineData, AI, Simulation & Analytics
Master’s in Real-Time Analytics and Stream ProcessingEnrol
Related programmes

More in Data, AI, Simulation & Analytics

Master’s in Optimisation under Uncertainty EngineeringView programme →Master’s in Time Series, Signals and Edge AI EngineeringView programme →Master’s in Graph ML Engineering for Networks and SystemsView programme →Master’s in Optimisation under UncertaintyView programme →Master’s in Reinforcement & Imitation Learning for Control EngineeringView programme →Master’s in AI Governance and Compliance EngineeringView programme →

All Data, AI, Simulation & Analytics programmes → · How to apply · Fees & financing · Scholarships

Scroll to Top
SEIUM – Institute of Advanced Engineering
Powered by  GDPR Cookie Compliance
Privacy Overview

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.