Introduction to NoSQL Data Modeling
The NoSQL data modeling is defined as the NoSQL database allowing us to reserve many data. That data can be accessed from any locality and appliance, but we cannot decide which data modeling technique is good for our needs. Moreover, the NoSQL data model has been constructed for building high-efficient and speed queries in which it creates millions of queries in a second that can happen because all data has been contained in a table; hence the joins and cross-validation will not be performed massively.
What is NoSQL Data Modeling?
The NoSQL data modeling technique has played an important role in well-disciplined techniques like logical to physical mapping, normalization, and denormalization; nowadays, the data modeling is not efficiently studied both in practice and theory due to the insufficiency of systematic theory; it has different types in which it can able to reserve the data in the key-value form which is a key-value store that has been built to create queries as per the requirement and if the requirement is high then also it is helpful, another type allows to store the information in the form of a document, next type of it is allows to store data in the form of columns, and also this data modeling allows to store data in the graph form in which it has the relationship between data.
Types of NoSQL Data Modeling
Below are the types of NoSQL Data Modeling:
1. Key-value Store
This type of data modeling has been constructed for high-production requirements, as like as not, but this is the most familiar data model in which it can use the key values with pointers for reserving the data. Although the pointer is unique and can tie up with a section of data, it allows using an empty string as a value key.
2. Document-based Store
The SQL does not need to reserve the information; it does not need to know where the document has been stored, as SQL, XML, and JSON serve to be bound together, which can slow down the queries.
The data has been stored in a table in this store, so it does not require cross-referencing; it’s stored in a document similar to a key-value store.
3. Column-based Store
This data model can able to reserve the information in columns and not in rows which can normally happen in the SQL; in this data model, generally, data has been reserved in the columns, which are further grouped into families, that families further divided into more columns so that it can generate the nest of columns.
4. Graph-based Store
This data model is a network data model with a relationship between any two pieces of data; such a type of data model can be created for any data we want to constitute on a graph. It can utilize the relationship and node, in which the data is the node, and the relationship will be generated between nodes.
NoSQL Data Modeling Basic Principles
There are some basic principles,
- It has a principle that they have a nice design; hence denormalization and data duplication will not allow.
- Because of the data duplication, it has been copying the content into various documents to overcome it.
- And because of the denormalization, the user can modify the particular data in the particular data mode.
NoSQL Data Modeling Techniques
There are three modeling techniques:
1. Conceptual Techniques
- Denormalization: It is quite an easy technique that can be used for copying the data into multiple tables for clarification; it can group all the data in one place, which needs to be for querying.
- Aggregates: In this technique, users can form a complicated entity structure in which aggregation can be overcome by keeping down the one-to-one relationship in which most data models have some standard techniques.
- Application side joins: The NoSQL does not assist the joins as they are question-oriented in which joins have been done at the design time, which can be compared with the relational databases.
2. General Modeling Techniques
- Enumerable keys: In NoSQL data modeling, the ungraded key values are functional because the approach can be over the dedicated servers uniformly; there is a need to add some configuration by sorting the features of the ordered key.
- Dimensionality reduction: It can be used to overcome the map of multi-dimensional data into a key-value model.
- Index table: This technique has been used only for storing purposes, not for supporting them internally.
- Composite key index: This common technique is useful when the ordered keys are in use.
- Inverted search-direct aggregation: This technique has been used to fulfill a particular set of standards.
3. Hierarchy Modeling Techniques
- Tree aggregation: This technique has worked with a single document that can be structured when it is treated with one record and when it can access one record.
- Adjacency lists: It is the simple technique in which nodes are modeled as an individualistic array record with the direct predecessor.
- Materialized paths: This technique is a type of denormalization that can be used to avoid repetitive tree structure traversing.
- Nested sets: This technique has been used in tree-like structures in relational databases, which can be relevant to NoSQL and key-value or document databases.
- Nested document flattening: This technique has been used for mapping complex structures to plain documents.
In this article, we conclude that NoSQL data modeling provides a way that can allow storing the data with the help of a particular technique, we have also discussed the types, techniques, and principles of NoSQL data modeling, so this article will help to understand the concept of it.
This is a guide to NoSQL Data Modeling. Here we discuss the definition, types, basic principles, and top 3 Data Modeling techniques. You may also look at the following articles to learn more-