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Quest Training

Real-Time Data Streaming & Analytics Training Course

Covers designing event-driven architectures that ingest, process and analyse continuous data streams to support monitoring, automation and rapid response.

BD · Big Data & Modern Data TechnologiesAll Levels#2557ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Data, Analytics & Digital TransformationCategories:Big Data & Modern Data Technologies

Course Overview

This advanced practical course develops the knowledge and skills required to design, process, and analyse real-time data streams within modern data environments. It focuses on transforming continuously generated data into timely insights that support operational decisions, monitoring, automation, and rapid response. Participants will explore real-time data architectures, streaming sources, event-driven processing, message flows, stream processing, window-based analytics, data quality, and real-time visualization. The programme emphasizes the architectural and analytical decisions required to build reliable streaming solutions rather than focusing on a single technology. The course also addresses event processing, latency management, scalability, fault tolerance, data integration, monitoring, and security. Participants will learn how to manage continuous data flows while maintaining reliable and meaningful analytical results. Through practical scenarios, participants will design streaming pipelines, analyse real-time events, identify operational patterns, develop real-time metrics, and evaluate architectures for high-volume and low-latency data workloads.

Learning Objectives

  • Analyse the principles and business value of real-time data streaming and analytics.
  • Identify common sources and characteristics of continuously generated data.
  • Evaluate architectures for real-time data ingestion and processing.
  • Design event-driven data flows for operational and analytical use cases.
  • Apply stream processing and window-based analytical techniques.
  • Manage data quality and consistency within continuous data pipelines.
  • Evaluate latency, throughput, scalability, and reliability requirements.
  • Apply approaches for handling late, duplicated, missing, or out-of-order events.
  • Develop real-time monitoring metrics and analytical indicators.
  • Integrate streaming data with data lakes, Lakehouses, and analytical platforms.
  • Evaluate security, governance, and operational monitoring requirements.
  • Develop practical real-time analytics solutions aligned with organizational needs.

Who Should Attend

This course is designed for Data Engineers, Data Architects, Data Scientists, Analytics Engineers, Software Engineers, Cloud Engineers, BI professionals, and IT specialists involved in real-time data platforms and analytics. It is also suitable for professionals working in operations, monitoring, IoT, digital transformation, financial services, telecommunications, manufacturing, logistics, and other environments where rapidly changing data must be analysed and acted upon. The programme is particularly relevant to organizations developing real-time monitoring, event-driven applications, operational intelligence, fraud detection, IoT analytics, predictive services, or low-latency decision-support capabilities.

Learning Outcomes

  • Explain the principles and architecture of real-time data streaming.
  • Identify appropriate streaming sources and event structures.
  • Design scalable data ingestion and streaming pipelines.
  • Apply event-driven processing concepts to real-time workloads.
  • Perform window-based and continuous stream analysis.
  • Identify patterns, anomalies, and operational events in streaming data.
  • Manage late, duplicated, missing, and out-of-order events.
  • Evaluate streaming systems according to latency, throughput, scalability, and reliability.
  • Develop real-time metrics and monitoring indicators.
  • Integrate streaming environments with modern data platforms and analytical systems.
  • Establish appropriate security, governance, and monitoring controls.
  • Develop a practical architecture for a real-time streaming and analytics use case.

Course Outline

Day 1

Real-Time Data Streaming Foundations & Architecture

  • Principles and characteristics of real-time data.
  • Streaming data versus batch data processing.
  • Event-driven architecture concepts.
  • Common streaming data sources and event structures.
  • Real-time ingestion and message flow.
  • Streaming architecture components.
  • Data producers, consumers, brokers, and processing services.
  • Latency, throughput, scalability, and availability.
  • Selecting architectures according to business requirements.
  • Practical Application: Design a conceptual real-time streaming architecture for a selected operational use case.
Day 2

Stream Processing & Event-Driven Data Pipelines

  • Fundamentals of continuous stream processing.
  • Event processing and transformation.
  • Filtering, mapping, aggregation, and enrichment.
  • Stateful and stateless processing.
  • Event-time and processing-time concepts.
  • Windowing techniques for continuous analytics.
  • Managing high-volume data streams.
  • Building reliable streaming pipelines.
  • Handling failures and processing interruptions.
  • Practical Application: Develop a real-time processing flow that transforms and aggregates continuous event data.
Day 3

Real-Time Analytics, Data Quality & Event Intelligence

  • Real-time analytical methods and use cases.
  • Continuous aggregation and metric generation.
  • Detecting patterns and anomalies in streaming data.
  • Real-time thresholds and event detection.
  • Handling missing, duplicated, late, and out-of-order events.
  • Data quality validation within streaming pipelines.
  • Maintaining consistency and analytical accuracy.
  • Developing operational and performance indicators.
  • Translating streaming events into actionable insights.
  • Practical Application: Analyse a continuous data stream and develop real-time indicators and event-detection rules.
Day 4

Scalability, Reliability, Integration & Monitoring

  • Scaling real-time streaming environments.
  • Managing throughput and processing latency.
  • Fault tolerance and recovery strategies.
  • Data delivery and processing reliability.
  • Integrating streaming platforms with Data Lakes and Lakehouses.
  • Connecting streaming pipelines with analytical and business intelligence platforms.
  • Real-time monitoring and operational observability.
  • Performance bottleneck identification.
  • Security and access management for streaming environments.
  • Practical Application: Evaluate a streaming architecture, identify performance and reliability risks, and develop improvement actions.
Day 5

Real-Time Analytics Strategy & Enterprise Implementation

  • Designing enterprise real-time analytics architectures.
  • Selecting appropriate streaming patterns for different use cases.
  • Integrating streaming analytics with operational systems.
  • Supporting automation and real-time decision-making.
  • Governance and lifecycle management for streaming data.
  • Defining operational and analytical performance indicators.
  • Managing implementation risks and adoption challenges.
  • Establishing monitoring and continuous improvement practices.
  • Developing scalable real-time analytics roadmaps.
  • Final Workshop: Design and present an integrated real-time data streaming and analytics solution covering data sources, ingestion, processing, event analysis, quality controls, monitoring, security, scalability, and implementation priorities.

Upcoming Dates

Selected date

Casablanca · 29 Mar – 2 Apr 2027 · Classroom

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CityDatesPrice per ParticipantEarly Bird
IstanbulScheduled
2 – 6 Nov 2026€4,700—
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Kuala LumpurScheduled
9 – 13 Nov 2026€4,400—
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LisbonScheduled
16 – 20 Nov 2026€5,400—
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LondonScheduled
23 – 27 Nov 2026€5,600€5,040until 2 Nov
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MadridScheduled
30 Nov – 4 Dec 2026€5,400€4,860until 9 Nov
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MadinahScheduled
30 Nov – 4 Dec 2026€4,200€3,780until 9 Nov
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MakkahScheduled
7 – 11 Dec 2026€4,200€3,780until 16 Nov
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ManamaScheduled
14 – 18 Dec 2026€4,700€4,230until 23 Nov
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MarbellaScheduled
21 – 25 Dec 2026€5,400€4,860until 30 Nov
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MarrakeshScheduled
28 Dec 2026 – 1 Jan 2027€4,300€3,870until 7 Dec
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MilanScheduled
4 – 8 Jan 2027€6,000€5,400until 14 Dec
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MuscatScheduled
11 – 15 Jan 2027€5,300€4,770until 21 Dec
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MunichScheduled
11 – 15 Jan 2027€5,400€4,860until 21 Dec
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OnlineScheduled
17 – 21 Jan 2027€1,700€1,530until 28 Dec
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ParisScheduled
25 – 29 Jan 2027€5,700€5,130until 4 Jan
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RiyadhScheduled
1 – 5 Feb 2027€4,200€3,780until 11 Jan
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RomeScheduled
8 – 12 Feb 2027€5,800€5,220until 18 Jan
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SalalahScheduled
15 – 19 Feb 2027€5,300€4,770until 25 Jan
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ViennaScheduled
22 – 26 Feb 2027€5,700€5,130until 1 Feb
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AmmanScheduled
28 Feb – 4 Mar 2027€3,100€2,790until 8 Feb
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AmsterdamScheduled
1 – 5 Mar 2027€5,400€4,860until 8 Feb
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BarcelonaScheduled
8 – 12 Mar 2027€5,400€4,860until 15 Feb
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BerlinScheduled
15 – 19 Mar 2027€5,800€5,220until 22 Feb
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CairoScheduled
22 – 26 Mar 2027€3,300€2,970until 1 Mar
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CasablancaScheduled
29 Mar – 2 Apr 2027€4,300€3,870until 8 Mar
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DammamScheduled
5 – 9 Apr 2027€4,200€3,780until 15 Mar
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DohaScheduled
12 – 16 Apr 2027€4,400€3,960until 22 Mar
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DubaiScheduled
18 – 22 Apr 2027€4,400€3,960until 29 Mar
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FrankfurtScheduled
19 – 23 Apr 2027€5,800€5,220until 29 Mar
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GenevaScheduled
26 – 30 Apr 2027€6,300€5,670until 5 Apr
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Available in: Istanbul · Dubai · London · Riyadh · Doha · Amsterdam

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