Snowflake vs traditional data warehouses - Visual Side-by-Side Comparison

Process Flow - Snowflake vs traditional data warehouses

Data Input

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Traditional Warehouse

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Storage + Compute tightly coupled

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Scaling requires downtime

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Query Execution

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Data Input

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Snowflake

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Storage separated from Compute

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Independent scaling

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Query Execution

Data flows into either traditional or Snowflake warehouses. Traditional tightly couples storage and compute, limiting scaling. Snowflake separates them, allowing flexible scaling and faster queries.

Execution Sample

Snowflake

LOAD data INTO warehouse;
RUN query;
SCALE compute;
RUN query;

Shows loading data, running a query, scaling compute resources, and running the query again to see performance changes.

Process Table

Step	Action	Traditional Warehouse Behavior	Snowflake Behavior	Result
1	Load data	Data stored in combined storage-compute system	Data stored in separate cloud storage layer	Data ready for queries
2	Run query	Compute uses fixed resources; query runs with current capacity	Compute cluster runs query independently	Query executed
3	Scale compute	Requires downtime; scaling affects storage and compute together	Compute scaled up/down instantly without downtime	Resources adjusted
4	Run query again	Query runs with new capacity after downtime	Query runs immediately with new compute power	Faster query execution
5	End	Scaling limits and downtime may slow work	Flexible scaling improves performance and availability	Process complete

💡 Process ends after query runs with scaled compute resources in both systems

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	Final
Data Storage	Empty	Data loaded	Data loaded	Data loaded	Data loaded	Data loaded
Compute Resources	Fixed	Fixed	Fixed	Scaled up/down	Scaled up/down	Scaled up/down
Query Performance	N/A	N/A	Normal speed	Normal speed	Improved speed	Improved speed

Key Moments - 3 Insights

Why does traditional warehouse scaling cause downtime?

Because storage and compute are tightly linked, scaling requires stopping the system to adjust both together, as shown in execution_table step 3.

How does Snowflake achieve faster query execution after scaling?

Snowflake separates storage and compute, allowing compute to scale instantly without downtime, so queries run faster immediately after scaling (execution_table steps 3 and 4).

Is data moved when scaling compute in Snowflake?

No, data stays in storage; only compute resources change, enabling quick scaling without data transfer, as seen in variable_tracker for Data Storage.

Visual Quiz - 3 Questions

Look at the execution_table at step 3, what happens when scaling compute in a traditional warehouse?

A Scaling happens instantly without downtime

B Requires downtime and affects storage and compute together

C Only compute scales without affecting storage

D Data is moved to a new storage system

💡 Hint

Refer to execution_table row 3 under Traditional Warehouse Behavior

According to variable_tracker, what is the state of compute resources after step 4?

A Fixed and unchanged

B Removed completely

C Scaled up or down

D Data storage is scaled instead

💡 Hint

Check Compute Resources row after Step 4 in variable_tracker

If Snowflake did not separate storage and compute, how would the scaling behavior change?

A Scaling would require downtime like traditional warehouses

B Scaling would be faster and independent

C Data would be automatically duplicated

D Queries would run slower

💡 Hint

Compare Snowflake and Traditional behaviors in execution_table step 3

Concept Snapshot

Snowflake separates storage and compute, allowing independent scaling without downtime.
Traditional warehouses couple storage and compute, causing downtime during scaling.
Snowflake's architecture enables faster queries and flexible resource use.
Scaling compute in Snowflake does not move data.
This separation improves performance and availability.

Full Transcript

This visual execution compares Snowflake and traditional data warehouses. Data loads into both systems. Traditional warehouses combine storage and compute tightly, so scaling compute requires downtime and affects storage. Snowflake separates storage from compute, allowing compute to scale instantly without downtime. Queries run faster after scaling in Snowflake. Variables tracked include data storage, compute resources, and query performance. Key moments clarify why traditional scaling causes downtime and how Snowflake avoids it. Quiz questions test understanding of scaling behaviors and resource states. The concept snapshot summarizes the main differences and benefits of Snowflake's architecture.