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I🔥 Mastering EMR Cluster Resource Tuning for Big Data Workloads | Spark on AWS EMR Explained 🔥

In this video, I dive deep into how to allocate resources effectively in an AWS EMR cluster based on the size and complexity of your data workloads.

💡 What you'll learn:

How to decide the right number of executors, cores, and memory

Understanding and setting partitions efficiently

Common issues like GC overhead, OOM (OutOfMemory) errors, and Disk I/O bottlenecks

How to balance executor cores to reduce garbage collection pressure

Real-world best practices to avoid resource wastage and job failures

Whether you're working with Apache Spark on EMR, optimizing ETL pipelines, or running large-scale batch jobs, these tips will help you maximize performance and reduce costs.

🔧 Topics Covered:
00:00 - Introduction
01:15 - Key EMR Components & Architecture
03:20 - Calculating Executors, Cores, and Memory
07:45 - Partition Tuning Best Practices
10:10 - GC Overhead & OOM Error Handling
13:30 - Disk I/O Issues & Mitigation
16:00 - Summary & Key Takeaways

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#AWS #EMR #ApacheSpark #BigData #DataEngineering #PerformanceTuning #SparkOptimization #OOM #GCTuning #PartitionStrategy #AWSDataEngineer