Why Snowflake separates compute from storage - Visual Breakdown

Process Flow - Why Snowflake separates compute from storage

Data Stored in Central Storage

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Compute Clusters Access Storage

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Cluster A

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Process Queries

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Results Returned

Snowflake stores data centrally and lets multiple compute clusters access it independently to run queries.

Execution Sample

Snowflake

CREATE WAREHOUSE WH1;
CREATE WAREHOUSE WH2;
USE WAREHOUSE WH1;
SELECT * FROM table;
USE WAREHOUSE WH2;
SELECT * FROM table;

Two compute warehouses run queries on the same central data storage independently.

Process Table

Step	Action	Compute Cluster	Storage Access	Result
1	Create Warehouse WH1	WH1 created	No data accessed	Ready to run queries
2	Create Warehouse WH2	WH2 created	No data accessed	Ready to run queries
3	Run query on WH1	WH1 active	Reads data from central storage	Query result from WH1
4	Run query on WH2	WH2 active	Reads data from central storage	Query result from WH2
5	WH1 and WH2 run independently	Both active	Shared storage accessed concurrently	Results returned separately
6	Stop WH1	WH1 stopped	No storage access	No queries run
7	WH2 continues	WH2 active	Reads data from storage	Query results continue
8	End	WH1 stopped, WH2 active	Storage remains intact	System stable

💡 Execution stops as warehouses are stopped or queries complete; storage remains separate and persistent.

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 6	Final
WH1 State	Not created	Created	Created	Active	Active	Stopped	Stopped
WH2 State	Not created	Not created	Created	Created	Active	Active	Active
Storage State	Data stored	Data stored	Data stored	Accessed	Accessed	Accessed	Accessed

Key Moments - 3 Insights

Why can WH1 and WH2 run queries at the same time without interfering?

Because both warehouses access the same central storage independently, as shown in steps 3 and 4, they do not block each other.

What happens to data when a compute warehouse stops?

Data remains safe and unchanged in central storage, as seen in step 6 where WH1 stops but storage is still accessed by WH2.

Why is separating compute and storage beneficial?

It allows multiple compute clusters to scale and run queries independently without duplicating data, demonstrated by WH1 and WH2 running separately.

Visual Quiz - 3 Questions

Look at the execution table, what is the state of WH1 after step 6?

A Active

B Stopped

C Not created

D Paused

💡 Hint

Check the 'WH1 State' in variable_tracker after step 6.

At which step do both WH1 and WH2 run queries concurrently?

A Step 3

B Step 4

C Step 5

D Step 6

💡 Hint

Look for the step where both warehouses are active and accessing storage.

If storage was not separated, what would happen when WH1 stops?

A WH2 would also lose access to data

B WH2 would continue normally

C Data would be duplicated

D Queries would run faster

💡 Hint

Think about the benefit of separate storage shown in steps 6 and 7.

Concept Snapshot

Snowflake separates compute and storage.
Data is stored centrally and persistently.
Multiple compute warehouses access data independently.
Compute clusters can scale up/down without affecting storage.
This design improves concurrency and cost efficiency.

Full Transcript

Snowflake stores data in a central storage layer. Multiple compute warehouses can be created to run queries independently. Each warehouse accesses the same data without copying it. This separation allows warehouses to scale and run queries concurrently without blocking each other. When one warehouse stops, data remains safe and accessible by others. This design improves performance and cost by separating compute resources from storage.

Practice

1. Why does Snowflake separate compute from storage?

easy

A. To combine compute and storage for faster processing

B. To store data only on local machines

C. To allow independent scaling of compute and storage resources

D. To limit the number of users accessing data

Solution

1. Step 1: Understand Snowflake's architecture

   Snowflake separates compute (processing power) and storage (data saved) so they can work independently.

2. Step 2: Identify the benefit of separation

   This separation allows users to scale compute resources up or down without affecting stored data, improving flexibility and cost.

3. Final Answer:

   To allow independent scaling of compute and storage resources -> Option C

4. Quick Check:

   Separation means independent scaling = A [OK]

Hint: Think: compute and storage can grow separately [OK]

Common Mistakes:

• Confusing separation with combining compute and storage
• Thinking data is stored only locally
• Believing separation limits user access

2. Which of the following is the correct way to describe Snowflake's compute and storage separation?

easy

A. Compute resources can be paused without affecting stored data

B. Storage automatically scales with compute usage

C. Compute and storage are tightly coupled in one system

D. Compute and storage must always scale together

Solution

1. Step 1: Review compute and storage behavior

   Snowflake allows compute (warehouses) to be paused or resized without impacting stored data.

2. Step 2: Match the correct description

   Compute resources can be paused without affecting stored data correctly states compute can be paused independently, which is a key feature.

3. Final Answer:

   Compute resources can be paused without affecting stored data -> Option A

4. Quick Check:

   Compute pause independent of storage = C [OK]

Hint: Remember: compute can pause, storage stays safe [OK]

Common Mistakes:

• Thinking compute and storage are tightly linked
• Assuming storage scales automatically with compute
• Believing compute and storage must scale together

3. Consider this scenario: You run multiple queries on Snowflake using different virtual warehouses. What is the main advantage of Snowflake's compute-storage separation in this case?

medium

A. Queries run slower because compute and storage are separate

B. Each warehouse can scale independently without data duplication

C. Data must be copied for each warehouse to run queries

D. Storage costs increase with each warehouse

Solution

1. Step 1: Analyze multiple warehouses running queries

   Snowflake allows multiple compute clusters (warehouses) to access the same storage without copying data.

2. Step 2: Understand the benefit of independent scaling

   Each warehouse can scale or pause independently, improving performance and cost without duplicating data.

3. Final Answer:

   Each warehouse can scale independently without data duplication -> Option B

4. Quick Check:

   Independent scaling, no data copy = D [OK]

Hint: Multiple warehouses share storage, no copies needed [OK]

Common Mistakes:

• Assuming data is copied for each warehouse
• Thinking compute-storage separation slows queries
• Believing storage costs rise with more warehouses

4. You notice your Snowflake compute warehouse is running slowly. You try to scale up compute but the performance does not improve. What could be a reason related to compute-storage separation?

medium

A. Compute and storage must be scaled together to improve speed

B. Compute warehouses cannot be resized after creation

C. Scaling compute automatically scales storage too

D. Storage is the bottleneck, not compute, since they are separate

Solution

1. Step 1: Understand compute-storage bottlenecks

   Since compute and storage are separate, scaling compute won't help if storage speed limits performance.

2. Step 2: Identify the correct reason

   Storage is the bottleneck, not compute, since they are separate correctly points out storage could be the bottleneck even if compute is scaled.

3. Final Answer:

   Storage is the bottleneck, not compute, since they are separate -> Option D

4. Quick Check:

   Separate storage bottleneck limits speed = B [OK]

Hint: Slow queries? Check storage bottleneck, not just compute [OK]

Common Mistakes:

• Assuming compute and storage scale together
• Believing compute cannot be resized
• Thinking scaling compute always fixes performance

5. You want to optimize costs and performance in Snowflake by using multiple virtual warehouses for different teams. How does Snowflake's separation of compute and storage help you achieve this?

hard

A. You can pause or resize warehouses independently while sharing the same data storage

B. You must create separate copies of data for each warehouse to avoid conflicts

C. Storage costs increase with each warehouse you create

D. Compute and storage are combined, so scaling one scales the other automatically

Solution

1. Step 1: Understand cost and performance optimization

   Using multiple warehouses allows teams to work independently without interfering with each other.

2. Step 2: Apply compute-storage separation benefits

   Since compute and storage are separate, warehouses can be paused or resized independently while sharing the same data, saving costs.

3. Final Answer:

   You can pause or resize warehouses independently while sharing the same data storage -> Option A

4. Quick Check:

   Independent warehouse control with shared storage = A [OK]

Hint: Pause or resize warehouses without copying data [OK]

Common Mistakes:

• Thinking data must be copied for each warehouse
• Assuming storage costs rise with more warehouses
• Believing compute and storage always scale together