Spark & Databricks - Spark Architecture |Memory Management |Application Workflow (Theory) - Part 2 скачать в хорошем качестве

Spark & Databricks - Spark Architecture |Memory Management |Application Workflow (Theory) - Part 2

Welcome to DataToCrunch! 🚀 In this tutorial, we dive deep into Apache Spark's Architecture and its core components. Whether you're new to Spark or looking to strengthen your understanding, this video will guide you through Spark's inner workings, helping you grasp concepts critical for distributed data processing. What you'll learn in this video: ✅ An overview of Apache Spark's architecture ✅ Driver Node and Worker Node architecture in detail ✅ Memory management concepts: On-Heap vs Off-Heap Memory ✅ How garbage collection affects performance ✅ The complete Spark application workflow: From DAG creation to execution With structured explanations and visual aids, you'll gain a comprehensive understanding of how Spark processes large-scale data efficiently. By the end of this session, you'll be equipped to leverage Spark's architecture for scalable and optimized big data analytics. Timestamp - 00:00 - Intro 00:11 - Agenda 00:28 - Spark Architecture 03:12 - Driver Node Architecture 07:00 - Worker Node Architecture 09:47 - Memory Management Concept - a. On-Heap Memory 11:42 - Memory Management Concept - b. Off-Heap Memory 13:37 - On-Heap VS Off-Heap Memory 14:43 - Garbage Collection Concept 17:38 - Spark Application Workflow 21:49 - Coming Up Next Relevant Videos :    • Introduction to Apache Spark | Databricks ...   Please visit my related blogs : 1. https://datatocrunch.wordpress.com/20... 2. https://datatocrunch.wordpress.com/20... 🔔 Stay tuned for the next part, where we'll explore: 1.Spark APIs' history 2. Apache Spark ecosystem 3. RDD operations (transformations and actions) 4. Lazy evaluation and fault tolerance 5. Optimized execution with Directed Acyclic Graphs (DAGs) If you find this video helpful, don't forget to give it a thumbs up 👍 and subscribe to our channel for more tutorials like this. 🌟 Let’s keep crunching the data together! 🌟 #dataanalytics #dataengineer #spark #apachespark #garbagecollection #sparkworkflow #bigdata #databricks #databricksforbeginners