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

> 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 t…

- Web page: https://www.dataexpert.io/lesson/fact-data-modeling-day-3-lecture-v3
- Program: [Data Engineering Boot Camp V2 Combined Track](https://www.dataexpert.io/program/data-engineering-boot-camp-v2-combined-track)
- Module: Week 2: Fact Data Modeling in Iceberg
- Access: Requires enrollment in Data Engineering Boot Camp V2 Combined Track
- Length: 1 h 4 min video
- Skills: Data Modeling, ETL/ELT, Apache Spark
- Academy: DataExpert.io Academy

## About this lesson

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

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