# Fact Data Modeling - Minimizing Shuffle and Reducing Facts with Iceberg (Day 3 Lecture) - Pre-recorded from V3

> This is a pre-recorded lesson from V3. The lecture emphasizes the significance of minimizing shuffle when dealing with fact data. It explains how reducing shuffle can enhance the efficiency of data analysis and pipelines. Furthermore, the concept of reducing facts is explored, illustrating how it e…

- Web page: https://www.dataexpert.io/lesson/fact-data-modeling-day-3-lecture-v4
- Program: [Data Engineering Mastery Course](https://www.dataexpert.io/program/data-engineering-mastery-course)
- Module: Week 2: Fact Data Modeling in Iceberg
- Access: Requires enrollment in Data Engineering Mastery Course
- Length: 35 min video
- Skills: Data Modeling, ETL/ELT, Apache Spark
- Academy: DataExpert.io Academy

## About this lesson

This is a pre-recorded lesson from V3. The lecture emphasizes the significance of minimizing shuffle when dealing with fact data.  It explains how reducing shuffle can enhance the efficiency of data analysis and pipelines. Furthermore, the concept of reducing facts is explored, illustrating how it empowers analytics and data scientists to experiment and make informed, data-driven decisions. Students will be guided through various frameworks and strategies for handling big data, with a focus on making data more accessible and efficient for extended timeframes.[Recorded Nov 16, 2023]

## Navigation

- Previous lesson: [Fact Data Modeling - Core Concepts, Deduplication Techniques, and Retention Considerations with Iceberg (Day 1 Lab) - Pre-recorded from V3](https://www.dataexpert.io/lesson/fact-data-modeling-day-1-lab-v4.md)
- Next lesson: [Fact Data Modeling - Compact Tables for Efficient Data Representation with Iceberg (Day 2 Lab) - Pre-recorded from V3](https://www.dataexpert.io/lesson/fact-data-modeling-day-2-lab-v4.md)