Snowflake Data Warehouse Architecture
Snowflake mastersWhat is a Data Warehouse?
A Data Warehouse is a big storage place for data. It collects data from many sources, organizes it, and stores it in a clean way so companies can easily use it to take better decisions.
Imagine it like a library — but instead of books, it stores useful business data like customer info, sales records, website clicks, orders, payments, and more.
This system is specially made for business analysis, reporting, and decision-making. (snowflake Training in Hyderabad
Why Do Companies Need a Data Warehouse?
Let’s take an example. A company sells clothes online and offline. It collects data from
Website orders
In-store purchases
Mobile app
Payment systems
Customer feedback
Now imagine all that data is stored in different places. It becomes very hard to understand:
How many shirts sold last month?
Which store has the highest sales?
Which product is most loved by customers?
A Data Warehouse solves this problem by bringing all data into one place, cleaning it, organizing it, and making it easy to search and use.
That’s why companies need a data warehouse – to get a clear picture of their work and plan smartly.
Types of Data Warehouse Architecture
A data warehouse is like a big storage system where companies store all their important data. This data can be about sales, customers, employees, products, etc.
But how this data is organized, stored, and used depends on something called "architecture."
Think of architecture like the blueprint or structure of a building. In data warehousing, it’s the structure of how data flows from the source (where it comes from) to the place where people use it (like reports and dashboards).
1.Single-Tier Architecture
What is Single-Tier Architecture?
This is the simplest type of data warehouse architecture.
In this structure, everything happens in one place:
Data is collected
Data is stored
Data is used for reports and analysis
All of this is done in the same system.
Easy Example
Imagine you have only one notebook
You write your school notes
You solve your homework
You also prepare for exams — all in one notebook
That’s how single-tier architecture works — everything in one place.
Key Features
One system handles all tasks
Very basic structure
Used for very small applications
Advantages
Very simple to set up
Low cost
Easy for small systems or learning
Disadvantages
Not good for large data
Can become slow
Not very secure or efficient
Where It Is Used
Personal projects
Small test environments
Not common in big companies
2.Two-Tier Architecture
What is Two-Tier Architecture?
In this architecture, the work is divided into two parts or layers
One layer for storing the data (Data warehouse or database)
Another layer for using the data (like reports, charts, dashboards)
These two parts are connected directly to each other.
Easy Example
One notebook to collect all notes (data storage)
Another notebook to summarize and revise the notes (reporting)
So you have two notebooks, each with a different purpose — that’s two-tier.
Key Features
One system stores data
Another system shows the data to users
Advantages
Better than single-tier
Data is organized more clearly
Faster performance compared to single-tier
Disadvantages
Still limited in handling big data
Can become slow with too many users
Less secure than more advanced systems
Where It Is Used
Small to medium-sized companies
Simple business tools and small teams
3.Three-Tier Architecture
What is Three-Tier Architecture?
This is the most powerful and widely used architecture in the real world.
It has three layers, and each layer does a different job:
Tier 1: Bottom Layer – Data Source & Staging Area
This is where raw (original) data comes in
Data is collected from different places: apps, files, web, Excel, etc.
The data is cleaned and prepared (called ETL process)
Like cleaning vegetables before cooking.
This stage prepares data for storage.
Tier 2: Middle Layer – Data Warehouse Layer
The clean data is stored here in an organized way
Data is saved in tables and made ready for reporting
Can store a large amount of data safely for many years
Think of this as your fridge — clean food (data) stored neatly.
Tier 3: Top Layer – Presentation or Reporting Layer
This is where people use the data
They create reports, dashboards, and graphs using tools
Tools like Power BI, Tableau, Excel are used here
Think of this as the dining table — clean food is served ready to eat (data ready to use).
Key Features of Three-Tier
Data flows from source → staging → storage → reporting
Uses ETL (Extract, Transform, Load) tools
Used in modern data platforms like Snowflake, Redshift, BigQuery
Advantages
Very powerful and fast
Good for huge amounts of data
Many users can work at the same time
Strong security and backup options
Disadvantages
Costlier to set up
Needs trained people to manage
More complex than other models
Where It Is Used:
Big businesses like banks, hospitals, e-commerce, tech companies
Any company with lots of data and reporting needs
Components of Snowflake Data Warehouse Architecture
Snowflake is a cloud-based platform that helps companies to store, process, and analyze data. It is specially designed to handle a lot of data, and it works fast, even with very large data files.
Snowflake has three main parts (also called layers). Each part does a different job, and all three parts work together.
1. Storage Layer – (Where the Data is Saved)
What is it?
This is the part of Snowflake where all the data is stored. Think of it as a huge, safe storage room on the internet where you can keep any kind of data.
Simple Example
Imagine you are running a store. You keep records of:
Customers
Products
Sales
Payments
All this information is stored safely in the storage layer.
Types of Data You Can Store
Structured data: Like tables with rows and columns (example: Excel sheet)
Semi-structured data: Like JSON, XML (example: Web data)
Features
Data is compressed to use less space.
It’s stored automatically and safely.
You can store unlimited data without worry.
It uses cloud services like AWS, Azure, or Google Cloud.
2. Compute Layer (Virtual Warehouse) – (Where the Work Happens)
What is it?
This is the part where all the data work is done. Whenever you search for data or do calculations, this layer does the job.
Snowflake calls this the "Virtual Warehouse".
Example
Think of it like workers in a factory
They go into the storage room
Pick the right data
Process it
Give you the results
This is what the compute layer (Virtual Warehouse) does. It reads the data and does the processing.
What Can You Do Here?
Run SQL queries
Create reports
Generate dashboards
Do data analysis
Features
You can run many warehouses at the same time.
Each team can have their own warehouse.
You can pause or start a warehouse anytime.
You can increase or decrease the size depending on how fast you want the result.
3. Cloud Services Layer – (The Brain of the System)
What is it?
This is the control system of Snowflake. It handles everything behind the scenes. It does not store or process data directly, but it manages everything else.
Think of it like a manager in a company
Controls who can enter and what they can see
Gives tasks to workers
Keeps everything working smoothly
This layer makes smart decisions so everything runs without problems.
What Does It Do?
Handles user login and passwords
Controls who can access what data
Tracks all activities in Snowflake
Helps make SQL queries faster
Controls data sharing and automation
Features
Secure login with options like multi-factor authentication
Query optimization to give you quick answers
Metadata management (metadata is info about your data)
Access control with roles and permissions
How All 3 Parts Work Together
“How many items did we sell last month in Hyderabad?”
Cloud Services Layer: Checks if you are allowed to access the data.
Compute Layer: Goes to work by reading and processing the data.
Storage Layer: Provides the sales data saved inside.
The answer is shown on your screen in seconds.
This is how all 3 components work as a team.
Features of Snowflake Data Warehouse
What it means
Snowflake is not installed on your local computer or office server. It works completely on the internet (cloud). You don’t have to buy or manage hardware.
Why it's good
No need for physical machines.
Easy to access from anywhere.
You can increase or decrease the usage easily.
2.Separation of Storage and Compute
What it means
In Snowflake, data storage and data processing work separately. This means saving data (storage) and doing calculations (compute) are independent.
Why it's good
You can store a lot of data but use processing power only when needed.
It saves money and makes the system faster.
3.Auto-Scaling and Auto-Suspend
What it means
Snowflake can automatically increase its power when many users are working. When no one is using it, it can pause itself to save costs.
Why it's good
No need to manually monitor usage.
Saves money when not in use.
Automatically handles heavy workloads.
4.Time Travel
What it means
You can go back to the past version of your data (like undo). If you delete something by mistake, you can recover it.
Why it's good
Protects data from accidents.
Helps in audits and checking past records.
You can see data from hours or days ago.
5.Data Sharing
What it means
You can share your data with other Snowflake users safely without sending files.
Why it's good
No need to download or email files.
Instant sharing with high security.
Useful for business partners and teams.
6.High Security
What it means
Snowflake protects your data using advanced security methods like encryption, multi-factor authentication, and access controls.
Why it's good
Keeps sensitive data safe.
Only the right people can see the data.
Meets legal and business security standards.
7.Support for Structured and Semi-Structured Data
What it means
It can handle both regular (like tables with rows and columns) and non-regular data (like JSON, XML).
Why it's good
You can use all types of data in one place.
No need for different tools for different formats.
Saves time and effort.
8.Works with All Major Cloud Platforms
What it means
Snowflake runs on Amazon AWS, Microsoft Azure, and Google Cloud. You can choose your preferred platform.
Why it's good
Flexible for companies using different cloud services.
Easy to move or connect data across platforms.
9.Easy Integration with Tools
What it means
You can connect Snowflake with BI tools (like Tableau, Power BI), ETL tools (like Talend, Informatica), and programming languages.
Why it's good
Helpful for data analysts and developers.
Smooth connection with existing tools and apps.
Speeds up reporting and automation.
10.Zero Maintenance
What it means
Snowflake takes care of system upgrades, performance tuning, and backups on its own.
Why it's good
Saves time and effort for IT teams.
No downtime or manual updates.
Always running the latest version.
11.Scalability
What it means
Snowflake can grow with your data. If you have small or huge data, it handles both smoothly.
Why it's good
No limit on storage or users.
Good for startups and big companies.
Pay only for what you use.
12.Multi-Cluster Warehouses
What it means
It can run many tasks at the same time without slowing down. Each task gets its own power.
Why it's good
Many people can work at the same time.
No delays or slow performance.
Ideal for large teams.
1.Cloud-Based System – No Hardware Needed
What it means
Snowflake is a cloud-based system. This means you don’t need to install any hardware (like servers) or software at your office. It runs completely on the internet using cloud platforms like AWS, Azure, or Google Cloud.
Why it is an advantage
No setup or maintenance of machines.
Saves money and space.
You can access your data from anywhere at any time.
Very useful for remote teams and work-from-home setups.
2.Separation of Storage and Compute – Saves Money and Improves Performance
What it means
Snowflake separates the storage (where your data is saved) from compute (the engine that processes your data). They work independently.
Why it is an advantage
You can store a lot of data without paying for compute until needed.
If you need only processing power (like for analysis), you don’t need to increase storage.
More flexibility and better performance.
Pay only for what you use.
3.Automatic Scaling – Grows With Your Needs
What it means
Snowflake can automatically increase or decrease its performance power based on your work.
Why it is an advantage
Handles both small and large workloads easily.
You don’t have to manually change any settings.
When more users log in, Snowflake adjusts itself to give fast performance.
Saves cost by reducing power when not in use.
4.Multi-Cluster Architecture – Supports Many Users at Once
What it means
Multiple users or teams can use Snowflake at the same time without slowing each other down.
Why it is an advantage
No delays or wait times when multiple people run reports or queries.
Great for big teams in large companies.
Everyone gets smooth performance even during peak hours.
5.Time Travel – Undo Mistakes Easily
What it means
Snowflake stores old versions of your data for a few days (default is 1 day, can go up to 90 days). You can “travel back in time” to restore your data.
Why it is an advantage
You can recover data that was deleted by mistake.
You can compare previous and current versions.
Useful during audits or troubleshooting.
6.Zero-Copy Cloning – Make Instant Copies
What it means
You can create a full copy of a table, schema, or database without using extra space or time.
Why it is an advantage
Saves time and storage space.
You can test changes without affecting the original data.
Good for development, testing, and backups.
7.High Security – Keeps Data Safe
What it means
Snowflake uses strong security features like data encryption, user roles, and multi-factor authentication.
Why it is an advantage
Keeps your data protected from hackers or unauthorized access.
Allows different access for different users (e.g., read-only or full access).
Meets industry security standards (good for finance, healthcare, etc.).
8.Supports All Data Types – Structured and Semi-Structured
What it means:
Snowflake can handle normal table data (like Excel sheets) and also complex data formats (like JSON, XML, Parquet).
Why it is an advantage
No need for separate tools for different data types.
You can store and analyze all types of data in one place.
Saves time, cost, and effort.
9.Easy Data Sharing – Share Without Sending Files
What it means
You can share your data with others (inside or outside your company) safely and instantly, without emailing or downloading files.
Why it is an advantage:
Saves time.
Reduces errors or version confusion.
Better for real-time collaboration between partners, clients, and teams.
10.Automatic Backups and Updates – No Downtime
What it means
Snowflake updates itself and takes regular backups automatically. You don’t need to worry about system crashes or old versions.
Why it is an advantage
Less work for IT teams.
No system downtime.
You always work with the latest version and most secure setup.
11.Integrates with Many Tools – Easy Connection
What it means
Snowflake connects with popular tools like Tableau, Power BI, Python, Informatica, Talend, and more.
Why it is an advantage
You can build reports, dashboards, and pipelines quickly.
Fits into your existing workflow.
Saves time during setup.
2.Fast Performance with Caching – Quick Results
What it means
Snowflake stores the results of recent queries in cache memory. If you run the same query again, it gives results quickly without recalculating.
Why it is an advantage
Very fast performance.
Saves time and compute cost.
Ideal for reports that run regularly.
3.Pay-as-You-Go – No Waste of Money
What it means
You pay only for what you use. There are no big upfront costs.
Why it is an advantage
Very cost-effective.
You can start small and grow later.
Good for startups and large enterprises alike.
14. Global Availability – Access Anywhere
What it means
Snowflake works in multiple cloud regions and data centers around the world.
Why it is an advantage
Supports global teams.
Keeps data near users for faster access.
Improves user experience.
What Is a Traditional Data Architecture?
A traditional data architecture is the old way of storing and using data. It is built on fixed hardware (like servers in your office) and usually requires
Manual work to increase storage or power.
Big upfront costs to buy machines and tools.
Separate systems for storing and analyzing data.
Time-consuming maintenance and updates.
What Is the Snowflake Data Cloud?
The Snowflake Data Cloud is a modern data warehouse built for the cloud. It is fully online and lets you store, share, and analyze large data easily. Snowflake uses cloud services like AWS, Azure, or Google Cloud and gives you
On-demand power and storage – no need to buy physical machines.
Pay for what you use – no waste of money.
Easy to use – quick setup and less manual work.
One platform – everything in one place: store, process, share, and analyze.
Main Differences: Snowflake vs. Traditional Architectures
1.Infrastructure (How They Are Built)
Traditional
Runs on physical servers.
Needs space, cooling, electricity.
Hard to scale (takes time to add more).
Snowflake
Runs in the cloud (no physical servers).
No setup needed – it’s ready to use.
Easy to grow or reduce power anytime.
Snowflake saves time, effort, and space.
2.Scalability (Handling More Data or Work)
Traditional
Adding storage or power takes time.
You might buy more than needed (waste).
Snowflake
Easily scales up or down based on need.
Pay only for what you use.
Snowflake is more flexible and cost-saving.
3.Maintenance (Keeping It Running)
Traditional
Needs a team to update, fix, or manage.
Downtime is possible during maintenance.
Snowflake
Managed by Snowflake team.
Automatic updates.
Almost zero downtime.
Snowflake is simple and low-maintenance.
4.Data Sharing
Traditional
Sharing data between systems is hard.
Often involves copying data manually.
Snowflake
Lets you securely share live data.
No need to move or copy.
Faster and safer data collaboration.
5. Performance and Speed
Traditional
Slower performance with big data.
Complicated tuning for better speed.
Snowflake
Fast queries, even with large data.
Uses smart caching and virtual warehouses.
Snowflake is quicker and easier to manage.
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Snowflake masters
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