Data Modeling for Analytics Engineering
Published 7/2026
Created by Maven Analytics • 1,500,000 Learners, Alice Zhao
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 61 Lectures ( 2h 0m ) | Size: 487.7 MB
Learn how analytics engineers transform OLTP databases into OLAP models that are optimized for reporting and analysis
What You Will Learn
Understand the fundamentals of data modeling for analytics engineering.
Design conceptual, logical, and physical data models.
Build scalable dimensional models using facts and dimensions.
Apply normalization and denormalization techniques appropriately.
Model data for business intelligence (BI) and analytics use cases.
Design star and snowflake schemas for efficient reporting.
Implement data modeling best practices for modern data warehouses.
Improve data quality, consistency, and governance through effective modeling.
Optimize models for query performance and scalability.
Gain practical experience with real-world analytics engineering workflows.
Why Take This Course?
Learn essential data modeling skills used in modern analytics engineering.
Build a solid foundation for working with data warehouses and BI platforms.
Improve your ability to design reliable, scalable analytics datasets.
Develop practical skills applicable to tools such as dbt, Snowflake, BigQuery, and Redshift.
Create data models that support faster reporting and business insights.
Understand industry best practices for organizing and managing analytical data.
Enhance your career prospects in analytics engineering, data engineering, and business intelligence.
Gain hands-on knowledge that can be applied immediately to real-world data projects.
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