Real World Use Cases for Tachyon - A Memory-Centric Distributed Storage System

This course covers the learning outcomes and goals of understanding the importance of memory in big data processing and how Tachyon, a memory-centric distributed storage system, addresses storage bottlenecks. The course teaches about Tachyon's features, architecture, deployment, security, and collaboration aspects. The individual skills or tools taught include utilizing memory-speed file sharing, integrating with cluster frameworks like Spark and Flink, and configuring storage tiers and policies. The teaching method involves presenting real-world use cases, comparisons, performance analysis, and hands-on examples. The intended audience for this course includes data engineers, big data developers, system architects, and professionals working with Apache Spark, MapReduce, or Apache Flink.