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Real-Time Data Processing Workshop: Using Apache Kafka and Spark for Live Analytics by Tonex

AI-based Software Inspection Essentials Training by Tonex

The Real-Time Data Processing Workshop by Tonex focuses on using Apache Kafka and Spark for live data analytics. This hands-on course equips participants with the skills to build, deploy, and optimize real-time data processing pipelines. Learn how to harness the power of Kafka and Spark for streaming analytics, ensuring timely insights and data-driven decision-making in dynamic environments.

Learning Objectives:

  • Understand the fundamentals of real-time data processing.
  • Explore the architecture of Apache Kafka and Spark.
  • Build and deploy real-time data pipelines.
  • Learn to process and analyze streaming data.
  • Optimize performance in live analytics systems.
  • Apply Kafka and Spark to industry-specific scenarios.

Audience:

  • Data engineers and analysts
  • Software developers and architects
  • IT professionals and system administrators
  • Business intelligence specialists
  • Researchers and data scientists
  • Anyone interested in real-time analytics

Course Modules:

Module 1: Introduction to Real-Time Data Processing

  • Basics of real-time vs batch processing
  • Key use cases for streaming analytics
  • Challenges in real-time data processing
  • Overview of tools for real-time systems
  • Introduction to Apache Kafka and Spark
  • Importance of live analytics in industries

Module 2: Understanding Apache Kafka

  • Kafka architecture and components
  • Kafka topics, partitions, and logs
  • Configuring and managing Kafka clusters
  • Producing and consuming messages in Kafka
  • Kafka for distributed data streaming
  • Ensuring reliability and scalability in Kafka

Module 3: Understanding Apache Spark for Streaming

  • Overview of Spark architecture
  • Spark Structured Streaming fundamentals
  • Transforming and analyzing streaming data
  • Managing Spark clusters and jobs
  • Spark integration with Kafka for live pipelines
  • Troubleshooting common Spark issues

Module 4: Building Real-Time Data Pipelines

  • Designing streaming data workflows
  • Setting up Kafka producers and consumers
  • Writing Spark applications for analytics
  • Integrating external data sources with pipelines
  • Monitoring and debugging data pipelines
  • Ensuring fault tolerance in pipelines

Module 5: Optimizing Real-Time Analytics Systems

  • Performance tuning in Kafka and Spark
  • Partitioning and parallelism strategies
  • Efficient memory and resource management
  • Handling data loss and recovery
  • Latency reduction techniques
  • Best practices for scaling analytics systems

Module 6: Real-World Applications and Case Studies

  • Real-time fraud detection in banking
  • Live analytics in e-commerce
  • Monitoring and alerting in IoT systems
  • Predictive maintenance with streaming data
  • Social media sentiment analysis in real-time
  • Lessons learned from industry implementations

Master the tools to harness real-time data insights. Enroll in the Real-Time Data Processing Workshop by Tonex and become proficient in Apache Kafka and Spark for live analytics. Contact Tonex now to secure your place!

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