Master’s in Real-Time Analytics and Stream Processing Engineering
Online master's programme of 1,900 hours in real-time analytics and stream processing: 12 modules covering event stream processing, distributed messaging, Apache Flink, streaming engines, real-time analytics, machine learning on streams, IoT streaming, system design, operations and data governance, with an assigned technical tutor.
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What you will be able to do
Design stream processing architectures with consistency guarantees.
Operate distributed messaging clusters such as Kafka.
Implement stateful pipelines with windows and event time in Flink.
Build real-time analytics with OLAP databases.
Apply online machine learning to data streams.
Optimise the performance and reliability of streaming systems.
Govern data in motion in compliance with the GDPR.
Who it's for
- Streaming engineers building low-latency pipelines with Kafka and Flink.
- Real-time data engineers moving from batch to continuous processing.
- Event-driven systems architects designing event sourcing and CQRS solutions.
- Real-time analytics engineers delivering live dashboards and alerts.
- Kafka platform engineers operating shared messaging clusters.
- Online data scientists deploying models that learn from streams.
- Engineering or science graduates aiming to move into banking and payments.
What you'll work with
- Regulation (EU) 2016/679 (GDPR) (EU General Data Protection Regulation)
- ISO/IEC 27001 (information security management systems)
- Directive (EU) 2022/2555 (NIS2) (cybersecurity of network and information systems)
- ISO/IEC 25012 (data quality model)
- PCI DSS v4.0 (payment card data security standard)
- ISO/IEC 20547-3 (big data reference architecture)
- Apache Flink
- Apache Kafka
- Spark Structured Streaming
- ClickHouse
- River
12 modules · 1900 h
1.1Data in motion versus data at rest1.2The Dataflow model and its guarantees1.3Event time versus processing time1.4Latency, throughput and backpressure1.5Log-centric Kappa architectures1.6Use cases: fraud, IoT and monitoring1.7Event patterns and event sourcing1.8CQRS and materialised views1.9Comparison of streaming engines1.10Case study: latency requirements for an anti-fraud system
2.1Apache Kafka architecture2.2Producers, consumers and consumer groups2.3Partitioning and message ordering2.4Replication and fault tolerance2.5Log retention and compaction2.6Apache Pulsar and Redpanda2.7Schemas with Avro and Protobuf2.8Schema evolution and compatibility2.9Security: TLS, SASL and ACLs2.10Lab: Kafka cluster with Schema Registry
3.1DataStream API and operators3.2Sliding, tumbling and session windows3.3Watermarks and late events3.4Keyed state and state backends3.5Checkpoints and savepoints3.6End-to-end exactly-once semantics3.7Complex event processing3.8Flink SQL and dynamic tables3.9Scaling and parallelism3.10Practical: user session detection with Flink
4.1Spark Structured Streaming4.2Kafka Streams and ksqlDB4.3Apache Beam and portability4.4Managed cloud services4.5Streaming SQL with Materialize and RisingWave4.6Lightweight engines for the edge4.7Micro-batching versus continuous processing4.8Selection criteria4.9Migrating between engines4.10Workshop: the same logic in Kafka Streams and Spark
5.1Incremental aggregations over time windows5.2Approximate estimation with HyperLogLog and Count-Min5.3Top-K and heavy hitters5.4Stream-stream and stream-table joins5.5Enrichment with reference data5.6Real-time OLAP databases5.7Apache Druid, Pinot and ClickHouse5.8Live dashboards5.9Rule-based alerting5.10Lab: live metrics with ClickHouse
6.1Online and incremental learning6.2Drift detection in streams6.3Real-time anomaly detection6.4Model inference in streaming pipelines6.5Real-time features6.6Training windows and forgetting6.7The River library for online ML6.8Prequential evaluation of online models6.9Feedback and delayed labels6.10Practical: online anomaly detector with River
7.1MQTT ingestion into Kafka7.2High-frequency time series7.3Edge processing7.4Clock synchronisation and out-of-order events7.5Compression and downsampling7.6Stream-fed digital twins7.7Real-time process alarms7.8Deterministic latency in process control7.9Integration with historians7.10Case study: live monitoring of a sensor fleet
8.1Domain event modelling8.2Partitioning and distribution keys8.3Idempotency and deduplication8.4The outbox pattern and CDC8.5Sagas and distributed coordination8.6Error handling and dead-letter queues8.7Reprocessing and replays8.8Testing streaming pipelines8.9Event contracts8.10Workshop: designing an event-driven order system
9.1Deployment on Kubernetes with operators9.2Monitoring lag and throughput9.3Tuning state and memory9.4Autoscaling based on event load9.5Load testing and benchmarks9.6Failure recovery9.7Zero-downtime upgrades9.8Cost and sizing9.9Observability with OpenTelemetry9.10Lab: load testing and tuning a Flink job
10.1Event catalogue and AsyncAPI10.2Lineage in continuous pipelines10.3Real-time data quality10.4Personal data and GDPR in streams10.5Encryption and access control10.6Retention and the right to erasure10.7Multi-tenancy in shared clusters10.8Auditing and compliance10.9Cybersecurity in messaging systems10.10Practical: AsyncAPI specification for a domain
11.1Payment fraud detection11.2Telecommunications network monitoring11.3Real-time recommendation11.4Logistics and fleet tracking11.5Financial markets and low latency11.6Cybersecurity and SIEM11.7E-commerce and dynamic pricing11.8Smart grids and real-time metering11.9Lessons from published architectures11.10Case study: end-to-end anti-fraud architecture
12.1Defining the real-time problem12.2Latency and volume requirements12.3Event and architecture design12.4Pipeline implementation12.5Analytics and models on the stream12.6Performance and failure testing12.7Operation and observability12.8Conclusions and architecture 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 Flink, Apache Kafka, Spark Structured Streaming, ClickHouse and River, in free, academic or evaluation versions. Real procedures are studied, such as real-time fraud detection, live monitoring of IoT sensors and operational dashboards with latencies of seconds. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: Regulation (EU) 2016/679 (GDPR), ISO/IEC 27001, Directive (EU) 2022/2555 (NIS2), ISO/IEC 25012, PCI DSS v4.0, ISO/IEC 20547-3.
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 Flink, Apache Kafka, Spark Structured Streaming, ClickHouse and River, 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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