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Architecting Big Data Applications: Real-Time Application Engineering

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Overview

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Learn about use cases and best practices for architecting real-time applications using big data technologies, such as Hazelcast and Apache Spark.

Syllabus

Introduction
  • Welcome
1. Real-Time Big Data
  • What is real time?
  • Real-time challenges
  • Strategies for real-time big data processing
2. Use Case 1: Social Media Sentiment Analysis (SM)
  • SM: Analyze the problem
  • SM: Outline the solution
  • SM: Consider technologies
  • SM: Lay out the architecture
  • SM: Design key elements
  • Best practices: Real-time streaming
3. Use Case 2: Real-Time Fraud Detection (FD)
  • FD: Analyze the problem
  • FD: Outline the solution
  • FD: Consider technologies
  • FD: Lay out the architecture
  • FD: Design key elements
  • Best practices: Predictive analytics
4. Use Case 3: Website Production Recommendations (PR)
  • PR: Analyze the problem
  • PR: Outline the solution
  • PR: Consider technologies
  • PR: Lay out the architecture
  • PR: Design key elements
  • Best practices: Parallel processing
5. Use Case 4: Mobile Couponing (MC)
  • MC: Analyze the problem
  • MC: Outline the solution
  • MC: Consider technologies
  • MC: Lay out the architecture
  • MC: Design key elements
  • Best practices: Pipeline management
Conclusion
  • Next steps

Taught by

Kumaran Ponnambalam

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