Real-Time Data Streaming and Event-Driven Analytics

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Real-Time Data Streaming and Event-Driven Analytics

Course Category : Data Management

An advanced programme for understanding streaming architectures and event-driven analytics, enabling organisations to process continuously arriving information and support faster, more responsive data-driven decisions.
Duration: 5 Days
Level: Advanced

Introduction

Modern digital systems increasingly depend on the ability to interpret data as it is generated rather than waiting for scheduled analytical cycles. Environments requiring rapid event detection, operational awareness, and timely decision-making therefore demand a structured understanding of streaming data, event processing, distributed architectures, and operational reliability.
This course develops advanced knowledge of real-time data streaming and event-driven analytics, covering event models, streaming architectures, stateful processing, time windows, delivery semantics, analytical integration, scalability, resilience, data quality, observability, and governance. It enables participants to evaluate architectural alternatives and establish robust approaches for enterprise streaming ecosystems..

Targeted Audience

  • Data Engineers and Data Platform Engineers
  • Data and Enterprise Solution Architects
  • Data and Advanced Analytics Professionals
  • Business Intelligence and Operational Analytics Specialists
  • Data Platform and Cloud Infrastructure Professionals
  • Distributed Systems and Integration Engineers
  • Digital Transformation and Data Leaders
  • Software Engineers working with event-driven systems

Targeted Skills

  • Real-Time Data Processing Requirements Analysis
  • Streaming and Event-Driven Architecture Design
  • Event, Message, and Data Stream Modelling
  • Stateful Processing and Windowing Analysis
  • Delivery Semantics, Consistency, and Reliability Assessment
  • Streaming and Analytical System Integration
  • Scalability, Performance, and Resilience Evaluation
  • Streaming Data Quality, Observability, and Governance

Expected Outcomes

  • Explain the architectural principles underlying streaming data processing systems.
  • Differentiate batch processing, stream processing, and event-driven architectures.
  • Design conceptual event-streaming architectures aligned with enterprise requirements.
  • Analyse event time, windowing, state management, and processing guarantees.
  • Evaluate the role of major streaming technologies within modern data ecosystems.
  • Determine performance, scalability, and resilience requirements for streaming systems.
  • Establish data quality, observability, and governance controls for real-time environments.
  • Align event-driven analytics with enterprise use cases and decision requirements.

Training Topics Index

  • Evolution from batch-oriented to continuous data processing
  • Events, messages, streams, and streaming data sources
  • Real-time data characteristics and latency requirements
  • Streaming, micro-batching, and batch processing models
  • Enterprise use cases for event-driven analytics

  • Architectural principles of Event-Driven Architecture
  • Producers, consumers, brokers, and event channels
  • Publish/Subscribe and message distribution models
  • Stream partitioning, ordering, and scalability
  • Event-based decoupling of services and enterprise systems

  • Stateless versus stateful stream processing
  • Event time, processing time, and latency concepts
  • Tumbling, sliding, and session windows
  • Watermarks and late-arriving event management
  • At-Most-Once, At-Least-Once, and Exactly-Once delivery semantics

  • Apache Kafka architecture and core concepts
  • Stream processing concepts with Apache Flink
  • Spark Structured Streaming concepts
  • Integration with data warehouses and data lake architectures
  • Platform selection based on scale, latency, reliability, and business requirements

  • Scalability, resilience, and fault tolerance
  • Throughput, latency, and consumer-lag monitoring
  • Streaming data quality and event-schema validation
  • Schema management, compatibility, and data-contract evolution
  • Governance, security, and streaming data lifecycle management

Course Features

  • Updated and Interactive Content
  • Hypothetical Examples and Case Studies
  • Pre- and Post-assessments to Measure Impact
  • Verified Certificate with a QR Verification Code