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Advanced Patterns and Big Data with Java Concurrency

Packt via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

3 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Overview

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Delve into advanced concurrency patterns and big data processing techniques using Java in cloud environments. Learn to apply proven patterns and frameworks to build high-performance, resilient cloud-native applications. This course explores advanced concurrency patterns such as leader-follower, circuit breaker, producer-consumer, and disruptor, demonstrating their application in cloud computing scenarios. Learners will also discover how Java integrates with big data technologies like Hadoop and Spark, optimizing performance and ensuring fault tolerance. The course empowers participants to architect scalable, data-driven solutions leveraging Java's concurrency capabilities. Learners engage with detailed explanations of advanced patterns, supported by practical examples and cloud-based use cases. The course emphasizes real-world applicability, guiding learners to connect theoretical patterns with big data and distributed processing challenges. This course is part two of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Java Concurrency and Parallelism, by Jay Wang.

Syllabus

Mastering Concurrency Patterns in Cloud Computing
- This module explores key concurrency patterns used in cloud computing, such as Leader-Follower, Circuit Breaker, and Bulkhead, to build resilient and scalable applications. Learners will gain insights into how these patterns optimize performance, manage failures, and streamline data flow in distributed systems. The content provides practical knowledge for implementing efficient and fault-tolerant cloud solutions.
Java and Big Data – a Collaborative Odyssey
- This module explores how Java is used in big data processing with Hadoop and Spark, focusing on scalable data pipelines, DataFrame APIs, and real-world applications like log analysis and fraud detection. Learners will gain hands-on knowledge of distributed computing frameworks and techniques for optimizing performance in big data environments.
Concurrency in Java for Machine Learning
- This module explores how to leverage Java's concurrency features to optimize machine learning workflows. Learners will gain hands-on knowledge of thread pools, parallel streams, and the Fork/Join framework to improve data processing and model training efficiency. The content also covers integrating deep learning libraries like DL4J for scalable ML solutions.