IEBS Digital School 7

Alejandro Pérez Pérez

This course provides an in-depth understanding of distributed data architectures, essential for handling large-scale data efficiently. Learn about parallelization, Map Reduce, Lambda and Kappa architectures, and how to manage resources using tools like HDFS, HIVE, and YARN. Practical case studies, such as Netflix's data architecture, are included to illustrate the application of these concepts.

Distributed Architectures in Big Data

Dive into the fundamentals of distributed architectures, focusing on parallelization and the Map Reduce programming model. Understand key concepts such as scalability, fault tolerance, and cluster geographic distribution.

Introduction to Distributed Architectures: Parallelization and Map Reduce

Explore the differences between Lambda and Kappa architectures and the concepts of batch and streaming data processing. Analyze the advantages and disadvantages of each architecture with a real case study of Netflix.

Lambda and Kappa Architectures. Batch vs Streaming

Master the use of Apache Foundation tools like HDFS, HIVE, YARN, and ZooKeeper. Learn about resource distribution and optimization in distributed environments through practical examples and a detailed study of Netflix's architecture.

Resource Management in Distributed Architectures

Explore the essentials of distributed architectures in Big Data, focusing on evaluating tools and combining them to enhance data processing. Gain practical skills in architectures like Lambda and Kappa, and understand their application in real-world scenarios.

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Distributed Architectures in Big Data

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