Over the last few years, companies started using modern applications with flexible data requirements to run their business activities. Relational Databases have predefined schemas and cannot satisfy the changing data needs. NoSQL Databases provide schema-less architecture making it easier for Developers to store data in a flexible manner.
Modern applications and websites use types of data structures that are very different from Relational Database modeling. Aggregate Data Models in NoSQL are used to meet the requirements and maintain smoothness in storing data appropriately.
The Aggregate Data Models in NoSQL Database allow easy handling of complex and list of nested records. In this article, you will learn about Aggregate Data Models in NoSQL Database, what are its different types, and their use cases. You will also go through an example of Aggregate Data Models in NoSQL.
What is a NoSQL Database?
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A NoSQL Database, also known as a non SQL or non-relational Database is a non-tabular Database that stores data differently than the tabular relations used in relational databases. Companies widely used NoSQL Database generally for big Data and real-time web applications. NoSQL Databases offers flexible schema, and it’s not mandatory to provide schema. As modern applications use a variety of changing data, so NoSQL Databases are best suited for them.
NoSQL Databases offer a simple design, horizontal scaling for clustering machines, and limit the object-relational impedance mismatch. It uses different data structures from those used by relational Databases making some operations faster. NoSQL Databases are designed to be flexible, scalable, and capable of rapidly responding to the data management demands of modern businesses.
Key Features of NoSQL Database
Some of the main features of the NoSQL Database are listed below:
- Horizontal Scaling: NoSQL Databases can scale horizontally by adding nodes to share loads. As the data grows the hardware can be added and scalability features could be preserved for NoSQL.
- Performance: Users can increase the performance of the NoSQL Database by adding a different server.
- Flexible Schema: NoSQL Databases do not require the same schema as compared to SQL Databases. The document in the same collection does not need to have the same set of fields and data type.
- High Availability: Unlike Relational Databases that use primary and secondary nodes for fetching data. NoSQL Databases use master place architecture.
To learn more about NoSQL Database.
What are Aggregate Data Models in NoSQL?
Aggregate means a collection of objects that are treated as a unit. In NoSQL Databases, an aggregate is a collection of data that interact as a unit. Moreover, these units of data or aggregates of data form the boundaries for the ACID operations.
Aggregate Data Models in NoSQL make it easier for the Databases to manage data storage over the clusters as the aggregate data or unit can now reside on any of the machines. Whenever data is retrieved from the Database all the data comes along with the Aggregate Data Models in NoSQL.
Aggregate Data Models in NoSQL don’t support ACID transactions and sacrifice one of the ACID properties. With the help of Aggregate Data Models in NoSQL, you can easily perform OLAP operations on the Database.
You can achieve high efficiency of the Aggregate Data Models in the NoSQL Database if the data transactions and interactions take place within the same aggregate.
Types of Aggregate Data Models in NoSQL Databases
The Aggregate Data Models in NoSQL are majorly classified into 4 Data Models listed below:
Key-Value Model
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The Key-Value Data Model contains the key or an ID used to access or fetch the data of the aggregates corresponding to the key. In this Aggregate Data Models in NoSQL, the data of the aggregates are secure and encrypted and can be decrypted with a Key.
Use Cases:
- These Aggregate Data Models in NoSQL Database are used for storing the user session data.
- Key Value-based Data Models are used for maintaining schema-less user profiles.
- It is used for storing user preferences and shopping cart data.
Document Model
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The Document Data Model allows access to the parts of aggregates. In this Aggregate Data Models in NoSQL, the data can be accessed in an inflexible way. The Database stores and retrieves documents, which can be XML, JSON, BSON, etc. There are some restrictions on data structure and data types of the data aggregates that are to be used in this Aggregate Data Models in NoSQL Database.
Use Cases:
- Document Data Models are widely used in E-Commerce platforms
- It is used for storing data from content management systems.
- Document Data Models are well suited for Blogging and Analytics platforms.
Column Family Model
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Column family is an Aggregate Data Models in NoSQL Database usually with big-table style Data Models that are referred to as column stores. It is also called a two-level map as it offers a two-level aggregate structure. In this Aggregate Data Models in NoSQL, the first level of the Column family contains the keys that act as a row identifier that is used to select the aggregate data. Whereas the second level values are referred to as columns.
Use Cases:
- Column Family Data Models are used in systems that maintain counters.
- These Aggregate Data Models in NoSQL are used for services that have expiring usage.
- It is used in systems that have heavy write requests.
Graph-Based Model
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Graph-based data models store data in nodes that are connected by edges. These Aggregate Data Models in NoSQL are widely used for storing the huge volumes of complex aggregates and multidimensional data having many interconnections between them.
Use Cases:
- Graph-based Data Models are used in social networking sites to store interconnections.
- It is used in fraud detection systems.
- This Data Model is also widely used in Networks and IT operations.
Steps to Build Aggregate Data Models in NoSQL Databases
Now that you have a brief knowledge of Aggregate Data Models in NoSQL Database. In this section, you will go through an example to understand how to design Aggregate Data Models in NoSQL. For this, a Data Model of an E-Commerce website will be used to explain Aggregate Data Models in NoSQL.
This example of the E-Commerce Data Model has two main aggregates – customer and order. The customer contains data related to billing addresses while the order aggregate consists of ordered items, shipping addresses, and payments. The payment also contains the billing address.
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If you notice a single logical address record appears 3 times in the data, but its value is copied each time wherever used. The whole address can be copied into an aggregate as needed. There is no pre-defined format to draw the aggregate boundaries. It solely depends on whether you want to manipulate the data as per your requirements.
The Data Model for customer and order would look like this.
// in customers
{
"customer": {
"id": 1,
"name": "Martin",
"billingAddress": [{"city": "Chicago"}],
"orders": [
{
"id":99,
"customerId":1,
"orderItems":[
{
"productId":27,
"price": 32.45,
"productName": "NoSQL Distilled"
}
],
"shippingAddress":[{"city":"Chicago"}]
"orderPayment":[
{
"ccinfo":"1000-1000-1000-1000",
"txnId":"abelif879rft",
"billingAddress": {"city": "Chicago"}
}],
}]
}
}
In these Aggregate Data Models in NoSQL, if you want to access a customer along with all customer’s orders at once. Then designing a single aggregate is preferable. But if you want to access a single order at a time, then you should have separate aggregates for each order. It is very content-specific.
Conclusion
In this article, you learnt about NoSQL Databases, Data Modeling, and Aggregate Data Models in NoSQL. You also read about different types of Aggregate Data Models in the NoSQL Database and went through an example of an E-Commerce website, and understood how Aggregate Data Models in NoSQL are used for storing data.
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Aditya Jadon is a data science enthusiast with a passion for decoding the complexities of data. He leverages his B. Tech degree, expertise in software architecture, and strong technical writing skills to craft informative and engaging content. Aditya has authored over 100 articles on data science, demonstrating his deep understanding of the field and his commitment to sharing knowledge with others.
