What 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.
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.
Main Benefits of a Data Warehouse
| Feature | Simple Explanation |
| Centralized Data | All data comes to one place from different departments |
| Clean and Organized | Data is cleaned and stored neatly for use |
| Fast Reports | Easy to generate quick reports and dashboards |
| Supports Better Decisions | Helps managers take correct business steps |
| Handles Big Data | Can manage huge amounts of data without slowing down |
| Historical Records | Stores data from the past years for future comparisons |
| Secure Storage | Data is protected and only available to authorized people |
What is the Architecture of a Data Warehouse?1.Data Source Layer – "Where Data Comes From"This is the starting point. Data is collected from different sources like:
This data is raw – not yet cleaned or ready to use. Think of it like vegetables brought from the market — they are useful, but you must clean and cut them before cooking. 2️.Data Staging Layer – "Cleaning and Preparing the Data"This is the area where the data is
This full process is called ETL (Extract, Transform, Load). Let’s understand ETL in simple terms · Extract: Get the data · Transform: Clean and organize the data · Load: Move the clean data to the warehouse for use Example If dates are written like “01/03/2025” and “March 1, 2025” in different systems, the staging layer will make them all look the same – “2025-03-01”. 3️.Storage Layer (Data Warehouse Layer) – "Where Clean Data is Stored"This is the main part of the data warehouse. Here the cleaned data is stored in tables and organized into rows and columns, just like Excel sheets. Data is structured properly, like
This layer allows you to keep years of data and easily connect different types of information. Example: You can connect customer data with product data and sales data to know: “Which customer bought which product and when?” 4️.Presentation Layer – "Where People Use the Data"This is the user access area. business users, analysts, and managers access the data using
They can · Create charts, graphs, and reports · Filter data based on date, city, product · Compare current data with last year · Find trends and patterns Example: The marketing team can see which campaign gave the best results by checking data from this layer. |
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 (Snowflake Masters)
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.