Snowflake on AWS: A Practical Guide to Modern Data Warehousing
Modern applications generate enormous amounts of data from databases, APIs, applications, logs, and business systems. Turning that data into useful insights requires a data platform that can scale without making infrastructure management unnecessarily complicated.
Snowflake on AWS: A Practical Guide to Modern Data Warehousing
Modern applications generate enormous amounts of data from databases, APIs, applications, logs, and business systems. Turning that data into useful insights requires a data platform that can scale without making infrastructure management unnecessarily complicated.
Snowflake is a cloud-base Snowflake on AWS: A Practical Guide to Modern Data Warehousing
d data platform designed for storing, processing, and analyzing large datasets. When used alongside Amazon Web Services (AWS), Snowflake can become an important part of a modern data architecture.
This article explains the basic architecture, how Snowflake works with AWS, common use cases, and a simple example pipeline.
What is Snowflake?
Snowflake is a cloud data platform that separates data storage from compute.
Traditional data warehouses often tie storage and compute resources together. Snowflake takes a different approach:
- Data is stored centrally.
- Compute resources are provided through virtual warehouses.
- Different workloads can use separate compute resources.
- Compute can be scaled independently from storage.
This architecture makes it easier to support multiple workloads without requiring every workload to compete for the same compute resources.
How Snowflake Works with AWS
Snowflake can run on AWS infrastructure, allowing organizations to combine Snowflake's data platform capabilities with AWS services.
A simplified architecture might look like this:
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Applications
|
v
+------------------+
| AWS Services |
| |
| S3 / Kinesis / |
| Lambda / Glue |
+--------+---------+
|
v
+------------------+
| Snowflake |
| |
| Data Storage |
| + |
| Compute |
+--------+---------+
|
v
+------------------+
| BI / Analytics |
| Dashboards / ML |
+------------------+
For example, an application could generate event data that is collected in Amazon S3. Snowflake can then load and transform that data for analytics.
Snowflake Storage and Compute
One of the most important concepts to understand is the separation between storage and compute.
Storage
Snowflake manages the underlying data storage. Users don't need to manually provision disks or manage storage capacity in the same way they would with a traditional database server.
Compute
Snowflake uses virtual warehouses for query processing.
A virtual warehouse provides the compute resources needed to execute SQL queries.
For example, you might have:
1
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3
4
5
6
7
8
9
10
11
ETL Warehouse
|
+---- Data transformations
Analytics Warehouse
|
+---- BI dashboards
Data Science Warehouse
|
+---- Analytical workloads
Because these workloads can use separate compute resources, a heavy transformation job does not necessarily have to slow down dashboard queries.
Using Amazon S3 with Snowflake
Amazon S3 is commonly used as a data lake or staging area in AWS architectures.
A typical pipeline could look like this:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Application
|
v
Amazon S3
|
v
Snowflake
|
v
SQL Transformations
|
v
Analytics Tables
|
v
BI Dashboard
For example, suppose an e-commerce application produces JSON order events.
An event might look like:
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5
6
7
{
"order_id": 1001,
"customer_id": 501,
"product": "Laptop",
"amount": 899.99,
"timestamp": "2026-10-05T10:00:00Z"
}
These events can be stored in Amazon S3 before being loaded into Snowflake.
Once the data is available in Snowflake, SQL can be used to transform and analyze it.
Example Snowflake Table
A simplified table could contain:
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7
CREATE TABLE orders (
order_id INTEGER,
customer_id INTEGER,
product VARCHAR,
amount DECIMAL(10,2),
order_timestamp TIMESTAMP
);
You could then calculate total revenue:
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2
3
SELECT
SUM(amount) AS total_revenue
FROM orders;
Or calculate revenue by product:
1
2
3
4
5
6
SELECT
product,
SUM(amount) AS revenue
FROM orders
GROUP BY product
ORDER BY revenue DESC;
This is where Snowflake becomes valuable: large amounts of data can be queried using familiar SQL.
A Simple AWS + Snowflake Data Pipeline
Let's consider a common architecture for an analytics application.
Step 1: Collect data
Applications generate events such as:
- User registrations
- Purchases
- Website activity
- Application logs
- IoT events
These events can be sent to AWS services.
Step 2: Store raw data
Amazon S3 can provide durable object storage for raw files.
For example:
1
2
3
4
5
s3://company-data/
raw/
orders/
customers/
events/
Keeping the original data can be useful because it allows teams to reprocess the data later.
Step 3: Load data into Snowflake
The data can then be loaded into Snowflake for analytical processing.
A common pattern is to maintain separate layers:
1
2
3
4
5
6
7
8
9
10
Raw Data
|
v
Staging
|
v
Transformed Data
|
v
Analytics / Reporting
Step 4: Transform the data
SQL transformations can clean and organize the raw data.
For example:
1
2
3
4
5
6
SELECT
customer_id,
COUNT(*) AS order_count,
SUM(amount) AS total_spent
FROM orders
GROUP BY customer_id;
Step 5: Analyze the results
The transformed data can be connected to business intelligence tools and dashboards.
Teams can then answer questions such as:
- Which products generate the most revenue?
- Which customers are most active?
- How has revenue changed over time?
- Which regions have the highest sales?
Snowflake vs Amazon Redshift
Snowflake and Amazon Redshift can both be used for cloud data warehousing, but they have different architectures and operational models.
| Feature | Snowflake | Amazon Redshift |
|---|---|---|
| Cloud platforms | AWS, Azure, Google Cloud | AWS |
| Storage/compute separation | Yes | Supported through modern Redshift architectures |
| SQL analytics | Yes | Yes |
| AWS integration | Strong | Native |
| Infrastructure management | Highly managed | Managed AWS service |
| Multi-cloud support | Yes | No, AWS-focused |
The right choice depends on the organization's existing AWS environment, requirements, team expertise, cost model, governance needs, and workload characteristics.
When Should You Consider Snowflake?
Snowflake can be a good choice when you need:
- A managed analytical data platform
- Scalable SQL analytics
- Separate compute resources for different workloads
- Centralized analytical data
- Integration with cloud data sources
- Support for large analytical workloads
It is especially useful when data engineering and analytics teams want to spend less time managing infrastructure and more time working with data.
Important Design Considerations
Using Snowflake does not automatically create a good data architecture. Several factors still need careful planning.
Data Modeling
Good table design can significantly improve usability and query performance.
Consider whether your analytical model should use:
- Fact tables
- Dimension tables
- Star schemas
- Denormalized analytical tables
Security
Sensitive data should be protected using appropriate access controls.
Consider:
- Role-based access
- Least-privilege permissions
- Encryption
- Data masking
- Network controls
- Auditing
Cost Management
Cloud analytics platforms make it easy to scale compute, but poorly designed workloads can increase costs.
Useful practices include:
- Monitoring warehouse usage
- Choosing appropriate warehouse sizes
- Avoiding unnecessary queries
- Using separate warehouses for different workloads
- Reviewing workloads regularly
A Practical Architecture
For a larger analytics environment, the architecture could evolve into:
1
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5
6
7
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9
10
11
12
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+----------------+
| Applications |
+-------+--------+
|
v
+----------------+
| AWS Ingestion |
| Services |
+-------+--------+
|
v
+----------------+
| Amazon S3 |
| Data Lake |
+-------+--------+
|
v
+----------------+
| Snowflake |
| |
| Raw |
| Staging |
| Curated |
+-------+--------+
|
+----------+----------+
| | |
v v v
BI Tools Data Science Reports
This architecture separates ingestion, storage, transformation, and analytics responsibilities.
Final Thoughts
Snowflake and AWS can work together to create a flexible modern data platform.
AWS services can handle application workloads, data ingestion, and object storage, while Snowflake provides a managed environment for analytical storage and SQL-based processing.
The most important concept to remember is simple:
Use the right service for the right workload.
Instead of building a single system that handles ingestion, storage, transformation, and analytics, modern architectures can divide those responsibilities across specialized services.
For organizations already using AWS, Snowflake can be an option for building scalable analytical workloads while keeping infrastructure management relatively simple.
As your data platform grows, concepts such as data modeling, security, governance, observability, and cost optimization become just as important as choosing the technology itself.Snowflake on AWS: A Practical Guide to Modern Data Warehousing
Modern applications generate enormous amounts of data from databases, APIs, applications, logs, and business systems. Turning that data into useful insights requires a data platform that can scale without making infrastructure management unnecessarily complicated.
Snowflake is a cloud-base Snowflake on AWS: A Practical Guide to Modern Data Warehousing
d data platform designed for storing, processing, and analyzing large datasets. When used alongside Amazon Web Services (AWS), Snowflake can become an important part of a modern data architecture.
This article explains the basic architecture, how Snowflake works with AWS, common use cases, and a simple example pipeline.
What is Snowflake?
Snowflake is a cloud data platform that separates data storage from compute.
Traditional data warehouses often tie storage and compute resources together. Snowflake takes a different approach:
- Data is stored centrally.
- Compute resources are provided through virtual warehouses.
- Different workloads can use separate compute resources.
- Compute can be scaled independently from storage.
This architecture makes it easier to support multiple workloads without requiring every workload to compete for the same compute resources.
How Snowflake Works with AWS
Snowflake can run on AWS infrastructure, allowing organizations to combine Snowflake's data platform capabilities with AWS services.
A simplified architecture might look like this:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Applications
|
v
+------------------+
| AWS Services |
| |
| S3 / Kinesis / |
| Lambda / Glue |
+--------+---------+
|
v
+------------------+
| Snowflake |
| |
| Data Storage |
| + |
| Compute |
+--------+---------+
|
v
+------------------+
| BI / Analytics |
| Dashboards / ML |
+------------------+
For example, an application could generate event data that is collected in Amazon S3. Snowflake can then load and transform that data for analytics.
Snowflake Storage and Compute
One of the most important concepts to understand is the separation between storage and compute.
Storage
Snowflake manages the underlying data storage. Users don't need to manually provision disks or manage storage capacity in the same way they would with a traditional database server.
Compute
Snowflake uses virtual warehouses for query processing.
A virtual warehouse provides the compute resources needed to execute SQL queries.
For example, you might have:
1
2
3
4
5
6
7
8
9
10
11
ETL Warehouse
|
+---- Data transformations
Analytics Warehouse
|
+---- BI dashboards
Data Science Warehouse
|
+---- Analytical workloads
Because these workloads can use separate compute resources, a heavy transformation job does not necessarily have to slow down dashboard queries.
Using Amazon S3 with Snowflake
Amazon S3 is commonly used as a data lake or staging area in AWS architectures.
A typical pipeline could look like this:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Application
|
v
Amazon S3
|
v
Snowflake
|
v
SQL Transformations
|
v
Analytics Tables
|
v
BI Dashboard
For example, suppose an e-commerce application produces JSON order events.
An event might look like:
1
2
3
4
5
6
7
{
"order_id": 1001,
"customer_id": 501,
"product": "Laptop",
"amount": 899.99,
"timestamp": "2026-10-05T10:00:00Z"
}
These events can be stored in Amazon S3 before being loaded into Snowflake.
Once the data is available in Snowflake, SQL can be used to transform and analyze it.
Example Snowflake Table
A simplified table could contain:
1
2
3
4
5
6
7
CREATE TABLE orders (
order_id INTEGER,
customer_id INTEGER,
product VARCHAR,
amount DECIMAL(10,2),
order_timestamp TIMESTAMP
);
You could then calculate total revenue:
1
2
3
SELECT
SUM(amount) AS total_revenue
FROM orders;
Or calculate revenue by product:
1
2
3
4
5
6
SELECT
product,
SUM(amount) AS revenue
FROM orders
GROUP BY product
ORDER BY revenue DESC;
This is where Snowflake becomes valuable: large amounts of data can be queried using familiar SQL.
A Simple AWS + Snowflake Data Pipeline
Let's consider a common architecture for an analytics application.
Step 1: Collect data
Applications generate events such as:
- User registrations
- Purchases
- Website activity
- Application logs
- IoT events
These events can be sent to AWS services.
Step 2: Store raw data
Amazon S3 can provide durable object storage for raw files.
For example:
1
2
3
4
5
s3://company-data/
raw/
orders/
customers/
events/
Keeping the original data can be useful because it allows teams to reprocess the data later.
Step 3: Load data into Snowflake
The data can then be loaded into Snowflake for analytical processing.
A common pattern is to maintain separate layers:
1
2
3
4
5
6
7
8
9
10
Raw Data
|
v
Staging
|
v
Transformed Data
|
v
Analytics / Reporting
Step 4: Transform the data
SQL transformations can clean and organize the raw data.
For example:
1
2
3
4
5
6
SELECT
customer_id,
COUNT(*) AS order_count,
SUM(amount) AS total_spent
FROM orders
GROUP BY customer_id;
Step 5: Analyze the results
The transformed data can be connected to business intelligence tools and dashboards.
Teams can then answer questions such as:
- Which products generate the most revenue?
- Which customers are most active?
- How has revenue changed over time?
- Which regions have the highest sales?
Snowflake vs Amazon Redshift
Snowflake and Amazon Redshift can both be used for cloud data warehousing, but they have different architectures and operational models.
| Feature | Snowflake | Amazon Redshift |
|---|---|---|
| Cloud platforms | AWS, Azure, Google Cloud | AWS |
| Storage/compute separation | Yes | Supported through modern Redshift architectures |
| SQL analytics | Yes | Yes |
| AWS integration | Strong | Native |
| Infrastructure management | Highly managed | Managed AWS service |
| Multi-cloud support | Yes | No, AWS-focused |
The right choice depends on the organization's existing AWS environment, requirements, team expertise, cost model, governance needs, and workload characteristics.
When Should You Consider Snowflake?
Snowflake can be a good choice when you need:
- A managed analytical data platform
- Scalable SQL analytics
- Separate compute resources for different workloads
- Centralized analytical data
- Integration with cloud data sources
- Support for large analytical workloads
It is especially useful when data engineering and analytics teams want to spend less time managing infrastructure and more time working with data.
Important Design Considerations
Using Snowflake does not automatically create a good data architecture. Several factors still need careful planning.
Data Modeling
Good table design can significantly improve usability and query performance.
Consider whether your analytical model should use:
- Fact tables
- Dimension tables
- Star schemas
- Denormalized analytical tables
Security
Sensitive data should be protected using appropriate access controls.
Consider:
- Role-based access
- Least-privilege permissions
- Encryption
- Data masking
- Network controls
- Auditing
Cost Management
Cloud analytics platforms make it easy to scale compute, but poorly designed workloads can increase costs.
Useful practices include:
- Monitoring warehouse usage
- Choosing appropriate warehouse sizes
- Avoiding unnecessary queries
- Using separate warehouses for different workloads
- Reviewing workloads regularly
A Practical Architecture
For a larger analytics environment, the architecture could evolve into:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
+----------------+
| Applications |
+-------+--------+
|
v
+----------------+
| AWS Ingestion |
| Services |
+-------+--------+
|
v
+----------------+
| Amazon S3 |
| Data Lake |
+-------+--------+
|
v
+----------------+
| Snowflake |
| |
| Raw |
| Staging |
| Curated |
+-------+--------+
|
+----------+----------+
| | |
v v v
BI Tools Data Science Reports
This architecture separates ingestion, storage, transformation, and analytics responsibilities.
Final Thoughts
Snowflake and AWS can work together to create a flexible modern data platform.
AWS services can handle application workloads, data ingestion, and object storage, while Snowflake provides a managed environment for analytical storage and SQL-based processing.
The most important concept to remember is simple:
Use the right service for the right workload.
Instead of building a single system that handles ingestion, storage, transformation, and analytics, modern architectures can divide those responsibilities across specialized services.
For organizations already using AWS, Snowflake can be an option for building scalable analytical workloads while keeping infrastructure management relatively simple.
As your data platform grows, concepts such as data modeling, security, governance, observability, and cost optimization become just as important as choosing the technology itself.
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