3 Data Modeling Mistakes That Can Derail a Team
All my FREE resources: https://www.skool.com/moderndata/about Consulting Services: https://go.kahandatasolutions.com ----- No data team is purposely trying to sabotage their own work. But there are some common data modeling mistakes I've noticed over the years that lead to data engineers (whether they mean to or not)... To accidentally derail their own work and effectiveness as a team. So in this video I'm going to share three examples of some of the most common mistakes I see made as it relates to data modeling so you can hopefully avoid some of them yourself. Or if these are things you notice already happening in your environment, you can take some steps to correct the situation before it gets too far off track. Enjoy. Timestamps: 0:00 - Intro 0:23 - Blending Concepts 2:26 - Wrong Granularity 4:57 - Lack of Clear Logic Flow Title & Tags: 3 Data Modeling Mistakes That Can Derail Your Work #kahandatasolutions #dataengineering #datamodeling
How to Track History in a Data Warehouse (Slowly Changing Dimensions)
The Data Movie | Data Literacy Explained Visually
Webinar: How AI is improving customer interactions and outcomes
Dimensional data modeling and idempotent pipelines in 78 minutes with DataExpert.io
Data Modeling Tutorial: Star Schema (aka Kimball Approach)
Understand the Data Vault Architecture
Data Modeling Interview Questions Answered! How to Nail a Data Modeling Interview!
Data Lake Modeling: 100 TBs into 5 TBs at Airbnb with Parquet + Run Length Encoding - DataExpert.io
The 3 Common Types of Fact Tables | Data Modeling 101
Why AI Has Failed to Take Your Job Since 1976
The 10 Modern Data Components - Essentials (Part 1)
Data Modeling in the Modern Data Stack
The Struggle of Data Modeling (Facts vs Dimensions)
Let's Compare the Kimball and Inmon Data Warehouse Architectures
Common Mistakes in Conceptual and Logical Data Modeling - Ellie.ai
System Design Explained: APIs, Databases, Caching, CDNs, Load Balancing & Production Infra
Don't Become a Data Analyst (Do THIS Instead)
The AI Take Over Has Completely Backfired and I Can't Be Happier
The Medallion Data Architecture (Pros & Cons)
