📌 Materialized Views vs Views vs Tables in Snowflake | When to Use Each (Performance + Cost)

Welcome back to AI Data Studio — where we make Snowflake, Data Engineering, and AI-assisted analytics simple and practical!

In this video, we break down one of the most important design decisions in Snowflake:
✅ Should you use a Table, a View, or a Materialized View?

Choosing the right option affects performance, storage cost, compute usage, and dashboard speed.
So if you're building analytics models, BI dashboards, or ETL pipelines in Snowflake, this guide will help you decide what to use and when.

🧠 What You’ll Learn:

✅ What Snowflake Tables are (and best use cases)
✅ What Views are (and why they don’t store data)
✅ What Materialized Views are (and how they boost performance)
✅ Performance differences between tables vs views vs materialized views
✅ Cost trade-offs: storage vs compute
✅ Best practices for BI dashboards and reporting layers

💻 Practical Examples Covered:

Using views for clean reporting logic

When tables are better for transformations / staging

When materialized views help improve dashboard refresh speed

Common mistakes that increase cost

🧰 Tools & Concepts Used:

Snowflake Web Console

Snowflake SQL

Query performance basics

Compute + storage cost considerations

🪄 Key Takeaways:

Tables = store data, flexible, good for ETL and staging

Views = reusable query logic, no storage, but can be slower if complex

Materialized Views = faster reads, but add storage + maintenance cost

📺 Watch Next:

▶️ Snowflake Streams & Tasks: Building Incremental Pipelines
▶️ Snowflake Time Travel & Fail-safe: How to Recover Data
▶️ Understanding Snowflake Storage vs Compute

💬 Join the Community:

What do you use most — Views or Tables?
Comment “MV” if you’d like a cheat sheet on when to use Tables vs Views vs Materialized Views in real-world projects.

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