About the Course
Organizations invest in real-time analytics because they need results they can prove in live operations: event ingestion reliability, latency control, schema consistency, alert accuracy, and dashboard freshness. In this field, you need to demonstrate capabilities in Kafka topic design, Spark Structured Streaming logic, windowed aggregation, data quality controls, and operational monitoring, all of which sit within a broader streaming architecture shaped by event-driven systems and cloud data platforms. The course aligns with practical patterns used in Apache Kafka, Azure Event Hubs, and Microsoft Fabric Real-Time Intelligence so you can connect concepts to production-style workflows.
This training turns scattered knowledge into a structured system for streaming data processing. You will practice designing ingestion flows, mapping event schemas, configuring stream transformations, building window-based metrics, and shaping alert rules, while being introduced to broader design choices such as Lambda and Kappa architecture, exactly-once processing concepts, and observability patterns at an overview level. What you will learn: you will learn how to plan a real-time pipeline, process events with Apache Spark Structured Streaming, and turn live data into operational dashboards and alerts. You will practice with architecture diagrams, sample event streams, and KPI definitions so you can produce credible design artefacts rather than abstract theory.
Real-time systems also come with constraints that matter in day-to-day delivery: data drift, late-arriving events, evolving schemas, platform cost, and pressure to keep reporting consistent across cloud services and hybrid environments. This course is built for professionals who must deliver under those conditions and who need practical methods that can be applied without overengineering the solution. It teaches real-time analytics and streaming data processing as an operational capability that supports faster decisions, cleaner event pipelines, and stronger collaboration between data teams and business users.
Target Audience
This course is designed for professionals who need to design, operate, or support live data pipelines and event-driven analytics in practical business settings.
- Data Engineer responsible for Kafka ingestion and stream reliability
- Analytics Engineer building windowed metrics and transformation logic
- BI Developer creating low-latency dashboards from event streams
- Solutions Architect shaping real-time analytics architecture and tool selection
- Cloud Data Platform Specialist managing event hubs and streaming services
- Streaming Data Engineer implementing Spark Structured Streaming jobs
- DataOps Engineer monitoring pipeline health and alert accuracy
- Product Analytics Lead tracking live customer behavior and event KPIs
- Operations Analyst using streaming metrics for operational decisions
- Data Platform Manager overseeing real-time analytics delivery and governance
Course Objectives
This course equips you to plan, design, and measure real-time analytics and streaming data processing initiatives that reduce latency, improve data reliability, and support timely operational decisions.
- Assess current streaming maturity using Kafka topic design, Azure Event Hubs, and event flow mapping.
- Apply windowed aggregation and watermarking in Apache Spark Structured Streaming to late-arriving data.
- Design a real-time analytics pipeline using Lambda architecture and Microsoft Fabric Real-Time Intelligence.
- Build an event schema and validation checklist for streaming data quality and consistency.
- Evaluate stream processing logic against latency, fault tolerance, and exactly-once processing requirements.
- Navigate data governance and operational stakeholder needs for live dashboards and alerting workflows.
- Implement KPI monitoring using stream metrics, dashboard refresh rates, and incident alert thresholds.
- Synthesize pipeline findings into a real-time architecture brief and reporting specification.
Requirements & Prerequisites
Before joining this course, you should have a working understanding of data pipelines, SQL fundamentals, and basic analytics concepts such as tables, joins, and dashboards. Familiarity with cloud data platforms and event data is helpful, but deep programming experience is not required; coding remains at a practical, guided level. Participants should bring a laptop with access to a browser-based lab environment or approved local tools, and should be prepared to work with sample streaming datasets, architecture diagrams, and hands-on exercises using Apache Kafka, Apache Spark Structured Streaming, Azure Event Hubs, and Microsoft Fabric Real-Time Intelligence. This course is designed for foundation to intermediate learners, with advanced implementation topics kept at operational rather than engineering depth.
Local Application and Business Return
How participants can apply the training in local operating conditions, and the return their organisation can plan for.
How participants apply this
Expected ROI
Training Methodology
This is a practical, outcome-driven course designed to turn real-time analytics and streaming data processing aspiration into measurable action and credible reporting.
Methodology includes:
- Hands-on calculation of stream latency and event throughput using sample Kafka metrics.
- Scenario simulation for a late-arriving events incident in a live retail feed.
- Diagnostic review using a streaming architecture checklist aligned to Azure Event Hubs patterns.
- Stakeholder mapping for data engineers, BI users, and operations owners in the reporting chain.
- Case study analysis across retail, financial services, manufacturing, and logistics streaming use cases.
- Group workshop to build a low-latency dashboard specification under time constraints.
- Reflection exercise comparing current pipeline practices against Kafka and Spark Structured Streaming benchmarks.
Upcoming Sessions
Next available dates worldwide
Certification
Recognized credentials that advance your career
Participants who complete the Real-Time Analytics and Streaming Data Processing Training Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.
NITA Accredited
Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.
CPD Certified
Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.
Why this course earns its place on your CV
Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.
Skills Relevance
- Master cutting-edge techniques in real-time data processing and analytics.
- Transform data into actionable insights with advanced streaming technologies.
- Stay competitive with skills in high-demand areas of data science and engineering.
Expert Delivery
- Learn from industry leaders with years of experience in big data and analytics.
- Courses designed and delivered by experts actively shaping the tech landscape.
- Exclusive access to live sessions and personalized feedback from data science pioneers.
Career Advancement
- Boost your career potential with certifications in trending tech skills.
- Equip yourself for senior roles in data analysis, enhancing your job prospects.
- Gain hands-on experience through real-world projects, building a job-winning portfolio.
Tools and platforms relevant to this field
Examples United Kingdom teams may encounter, and that may be featured in training where they support the confirmed course scope.
These are field-relevant examples, not a promise that every tool will be covered. Exact coverage depends on the confirmed course scope, participant needs, and delivery format.
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Apache Kafka Apache Software FoundationUsed for high-throughput event ingestion and decoupling producers from downstream consumers in streaming architectures.
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Apache Spark Structured Streaming Apache Software FoundationUsed to process streaming data with familiar DataFrame-based logic for transformations, aggregations, and near-real-time analytics.
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Azure Event Hubs MicrosoftUsed as a managed event ingestion service for cloud-scale telemetry and application event streams.
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Microsoft Fabric Real-Time Intelligence MicrosoftUsed to explore, query, and operationalise event streams alongside broader analytics workloads in Microsoft-first environments.
