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“Amazon EMR 7.5 Boosts Apache Spark and Iceberg Performance, Delivering 3.6x Faster Workloads Compared to Spark 3.5.3 and Iceberg 1.6.1”

# Amazon EMR 7.5 Boosts Apache Spark and Iceberg Performance, Delivering 3.6x Faster Workloads Compared to Spark 3.5.3 and Iceberg 1.6.1

Amazon Elastic MapReduce (EMR) has long been a cornerstone for organizations looking to process and analyze massive datasets in the cloud. With the release of **Amazon EMR 7.5**, Amazon Web Services (AWS) has introduced significant performance enhancements for **Apache Spark** and **Apache Iceberg**, two of the most widely used open-source frameworks for big data processing and analytics. According to AWS, workloads running on EMR 7.5 can achieve up to **3.6x faster performance** compared to Spark 3.5.3 and Iceberg 1.6.1, making it a game-changer for data-intensive applications.

This article explores the key improvements in Amazon EMR 7.5, the technical advancements behind the performance boost, and the implications for businesses leveraging Spark and Iceberg for their data processing needs.

## **What’s New in Amazon EMR 7.5?**

Amazon EMR 7.5 introduces a host of optimizations and updates that enhance the performance, scalability, and usability of Apache Spark and Iceberg. Here are the key highlights:

### 1. **Optimized Apache Spark 3.5.3**
Apache Spark is a distributed data processing engine widely used for big data analytics and machine learning. EMR 7.5 includes an optimized version of Spark 3.5.3, which incorporates several performance improvements:

– **Dynamic Partition Pruning Enhancements**: EMR 7.5 improves the efficiency of dynamic partition pruning, reducing the amount of data scanned during query execution. This is particularly beneficial for queries involving large datasets with complex partitioning schemes.

– **Adaptive Query Execution (AQE) Improvements**: AQE, a feature introduced in Spark 3.x, dynamically optimizes query plans at runtime. EMR 7.5 enhances AQE to better handle skewed data and improve join performance.

– **Improved Shuffle Performance**: The shuffle operation, a critical component of distributed data processing, has been optimized to reduce I/O overhead and improve data transfer speeds.

– **Native Integration with AWS Services**: EMR 7.5 further optimizes Spark’s integration with AWS services like Amazon S3, Amazon Redshift, and AWS Glue, enabling faster data ingestion and processing.

### 2. **Enhanced Apache Iceberg 1.6.1**
Apache Iceberg is an open table format designed for managing large-scale datasets in data lakes. EMR 7.5 includes an optimized version of Iceberg 1.6.1, which delivers the following benefits:

– **Faster Table Scans**: Iceberg’s table scan operations have been optimized to reduce latency and improve throughput, enabling faster query execution on large