Apache Spark: Cluster Computing with Working Sets

Join me as we dive into Apache Spark, a data analytics system introduced in a 2010 paper from Berkeley. Spark is a favorite among software engineers, data scientists, and machine learning engineers. It provides a single platform on top of multiple specialized systems like GraphX, MLlib, and Discretized Streams. In this video, we see how Spark's in-memory processing outpaces traditional MapReduce, achieving up to 1000x speed improvements, and explore its scalability, performance, and integration with technologies like Kubernetes. The core principles behind Spark's design are general-purpose data analytics, fault tolerance, and pluggability with various cluster managers and data sources. This video is for all engineers who want to understand Spark's architecture and use cases. 00:00 What is Apache Spark? 01:56 Key Features 02:50 High-Level Design 06:30 Data Flow 09:44 Conclusion 10:13 Interesting News! References: Spark Paper: https://people.csail.mit.edu/matei/papers/2012/nsdi_spark.pdf Older Paper: https://people.csail.mit.edu/matei/papers/2010/hotcloud_spark.pdf Mesos Paper: https://www.usenix.org/legacy/event/nsdi11/tech/full_papers/Hindman.pdf InterviewReady: http://interviewready.io/resources/ Courses: System Design Simplified Course: https://interviewready.io/course-page/system-design-course Low-Level Design Course: https://interviewready.io/course-page/low-level-design-course #ApacheSpark #SystemDesign #ResearchPaper