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

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🔔 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)

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🌟 Let’s keep crunching the data together! 🌟

#dataanalytics #dataengineer #spark #apachespark #garbagecollection #sparkworkflow #bigdata #databricks #databricksforbeginners

[Aravind-gzgx]

Neat and clear, waiting for next session