Document Data Modeling Technique – Used in NoSQL for Flexible and Nested Data
What is Document Data Modeling?
Document Data Modeling is a technique used in No SQL databases where data is stored as documents instead of rows and tables like in traditional relational databases. Each document contains all the information about a record and can include nested data and different data types.
These documents are usually stored in formats like JSON (JavaScript Object Notation), BSON, or XML. This model is highly flexible, scalable, and suitable for applications where data structure can change over time.
Key Characteristics of Document Data Modeling
- Data is stored in documents (like JSON)
- Documents can have nested structures (arrays, objects inside objects)
- Each document is self-contained and can vary in structure
- Supports schema-less design, meaning documents don’t have to follow a fixed structure
- Suitable for semi-structured and unstructured data
Simple Example
Let’s say you are building a database for an e-commerce application. A single product document might look like this (in a simplified format)(snowflake Training in Hyderabad)
json
CopyEdit
{
"product_id": "P123",
"name": "Wireless Mouse",
"price": 699,
"available": true,
"categories": ["Electronics", "Accessories"],
"specs": {
"color": "Black",
"battery": "AA",
"connectivity": "Bluetooth"
}
}
Here, the data is stored in one nested and flexible document, which is easier to read and manage compared to joining multiple tables in a relational model.
Where Document Data Models Are Used
- Content management systems (CMS)
- E-commerce product catalogs
- Blogging platforms
- Real-time analytics applications
- Mobile and web applications needing quick and flexible updates
Advantages of Document Data Modeling
- Flexible structure – you can store different fields in each document
- Faster performance for read and write operations
- Easier to model real-world objects
- No need for complex joins – all data is often stored together
- Scales well with large volumes of data across multiple servers
Limitations of Document Data Modeling
- Can lead to data duplication
- Querying can become complex if not well structured
- Schema-less nature may cause inconsistency if not managed properly
- Not ideal for highly relational data that needs many references
Popular NoSQL Databases That Use Document Modeling
- MongoDB
- CouchDB
- Amazon DocumentDB
- RavenDB
8.Key-Value Data Modeling Technique – Simple and Fast for Real-Time Applications
What is Key-Value Data Modeling?
The Key-Value Data Modeling technique is one of the simplest types of data models used in NoSQL databases. In this model, data is stored as a pair of two elements: a key and a value.
- The key is a unique identifier.
- The value is the data associated with that key.
It’s similar to how a dictionary or a map works in programming—each word (key) has a definition (value).
This model is designed for speed, simplicity, and scalability, making it a great choice for real-time applications where quick access to data is needed.(snowflake Training in Hyderabad)
Key Characteristics of Key-Value Data Modeling
- Stores data as key-value pairs
- Each key is unique
- The value can be a string, number, JSON, binary, or any format
- Extremely fast for both read and write operations
- No complex schema required
Simple Example
Let’s take an example of a shopping cart system for an online store
Key: "user_101_cart"
Value: {
"item1": "Laptop",
"item2": "Mouse",
"item3": "Headphones"
}
- The key is "user_101_cart" (identifying a user's cart)
- The value is the list of items in the cart
The system can quickly retrieve or update the user's cart using just the key.
Where Key-Value Models Are Used
- Real-time recommendation engines
- Session management (storing user sessions)
- Caching systems (temporary storage for quick access)
- IoT applications (sensors sending quick updates)
- High-performance gaming applications
Advantages of Key-Value Data Modeling
- Very fast data access (ideal for real-time apps)
- Simple structure and easy to implement
- Highly scalable across servers
- Excellent for use cases with simple data lookups
Limitations of Key-Value Data Modeling
- No relationships between data items
- Searching by value or conditions is difficult
- Not suitable for complex queries or multi-field filtering
- Lacks built-in support for data structure validation
Popular Key-Value Databases
- Redis
- Amazon DynamoDB
- Riak
- Berkeley DB
2.types of data modeling in data warehouse
- Star Schema – The Most Common Type of Data Modeling in Data Warehouse
The Star Schema is one of the most popular and simple types of data modeling in data warehouse systems. It is widely used in business intelligence (BI) and reporting because it is easy to understand and fast to query.
In this model, data is divided into two parts
Fact Table: This is the main table in the center. It stores numeric data like sales, revenue, quantity, etc. These are called facts because they are measurable.
Dimension Tables: These tables are placed around the fact table and store descriptive information like product names, customer details, dates, or locations. They help explain the facts.
Because the dimension tables are connected directly to the fact table, the structure looks like a star, which is why it’s called a Star Schema.
Key Features of Star Schema
- Simple and clear structure
- Easy to build and maintain
- Fast performance for queries and reports
- Best for read-heavy systems like dashboards and analytics tools
- Works well with tools like Power BI, Tableau, or Excel
Example
Let’s say you are working in a retail business. Your fact table might be called Sales_Fact and it stores
- Sales ID
- Product ID
- Date ID
- Store ID
- Total Sales
- Quantity Sold
Your dimension tables could be
- Product_Dim (Product Name, Category, Brand)
- Date_Dim (Date, Month, Year)
- Store_Dim (Store Name, City, State)
All these dimensions are linked to the Sales_Fact table using IDs (keys). This helps in running fast reports like
- "What were the total sales by product in April?"
- "How many items were sold in each store last month?
When to Use Star Schema
- You need quick reporting and easy data analysis
- Your team is using BI tools or SQL
- You want a simple model with high performance
- Your business data does not change too frequently
The Star Schema remains one of the best types of data modeling in data warehouse environments because it balances simplicity, performance, and usability.
- Snowflake Schema – A Detailed Type of Data Modeling in Data Warehouse
The Snowflake Schema is an advanced version of the star schema. It also has a central fact table, but the dimension tables are further divided into smaller related tables. This makes the structure look like a snowflake with more branches.
This type of data modeling in data warehouse systems is used when you want to remove duplicate data and create a more organized and normalized structure.
Key Features of Snowflake Schema
- Dimension tables are normalized into multiple levels
- Saves storage space by avoiding duplicate data
- More complex than the star schema
- Better for data accuracy and consistency
- Slightly slower than star schema for reporting
Example
Using the same retail example as before:
Your fact table
Sales_Fact with fields like
- Sales ID
- Product ID
- Date ID
- Store ID
- Total Sales
- Quantity Sold
Your dimension tables in snowflake style might look like
- Product_Dim has a Product Category ID
- Category_Dim stores details about categories
- Store_Dim has a City ID
- City_Dim stores city, state, and region info
This design creates more small tables but avoids repeating the same data over and over.
When to Use Snowflake Schema:
- You need a highly organized and clean database
- Your data has many repeating values
- Data storage optimization is important
- You're comfortable managing a more complex structure
- Accuracy and data consistency matter more than speed
Star Schema vs. Snowflake Schema
| Feature | Star Schema | Snowflake Schema |
| Structure | Simple and flat | More detailed and branched |
| Performance | Faster | Slightly slower |
| Storage | Uses more space | Uses less space |
| Complexity | Easy | More complex |
| Best for | Quick reporting | Large, normalized databases |
The Snowflake Schema is a powerful type of data modeling in data warehouse systems, especially when you need a clean, normalized, and scalable structure.
- Galaxy Schema (Fact Constellation) – Advanced Type of Data Modeling in Data Warehouse
The Galaxy Schema, also known as the Fact Constellation Schema, is a more advanced and flexible data modeling technique. It includes multiple fact tables that share some of the same dimension tables.
This type of data modeling in data warehouse systems is useful for large businesses that deal with different types of data and need to manage multiple processes at the same time.
Key Features of Galaxy Schema
- Contains more than one fact table
- Dimension tables are shared between fact tables
- Supports complex business systems
- Allows users to run reports across multiple data sets
- Can be used to model many-to-many relationships
Example
Let’s say you run a company that tracks both sales and inventory.
You could have two fact tables:
- Sales_Fact (Sales ID, Product ID, Customer ID, Date ID, Amount Sold)
- Inventory_Fact (Inventory ID, Product ID, Warehouse ID, Date ID, Quantity Available)
Both of these tables can share dimension tables like:
- Product_Dim (Product Name, Category, Brand)
- Date_Dim (Date, Month, Year)
- Location_Dim (Warehouse or Store Info)
This allows you to analyze how sales and inventory are related over time or by product.
When to Use Galaxy Schema:
- Your organization has multiple business areas to manage
- You need to track and compare different types of facts
- Your data warehouse handles very large and complex datasets
- You want to reuse dimensions across many reports and dashboards
Benefits of Galaxy Schema
- Helps manage real-world business complexities
- Avoids creating separate models for each process
- Improves data consistency across systems
- Enables cross-functional reporting
The Galaxy Schema is one of the most powerful types of data modeling in data warehouse design. It gives businesses the flexibility to combine and analyze multiple data sources in one model.