Fill in the chart below with five differences between SQL and NoSQL databases:
SQL NoSQL
structured query language 5. ocused on collection of documents.
Each table in a relational database has a predefined set of columns or fields, which define the attributes or properties of the data, and a set of rows or records, which represent the individual instances of the data. The columns in one table can be linked or related to columns in another table through the use of primary and foreign keys.
This structure allows for efficient querying and manipulation of data, as well as the ability to ensure data integrity through the use of constraints such as unique, not null, and foreign key constraints.
The relational model has been widely adopted and is used in many popular database management systems, including MySQL, Oracle, Microsoft SQL Server, and PostgreSQL.
A database schema is usually created by a database administrator or a developer and serves as a guide for other users who need to access or work with the database. It provides a clear understanding of the data model, which makes it easier to develop, test, and maintain the database.
In addition to defining the structure of the database, a schema can also include information about access control and security, such as user roles and permissions. Some database management systems also support multiple schemas, which allow different groups of users to work with different parts of the database.
Unlike relational databases, NoSQL databases do not use SQL for querying data and do not enforce the same level of data consistency and integrity as relational databases. This allows for more flexibility and scalability, as well as better performance when working with large volumes of data.
NoSQL databases are often used in web and mobile applications, where large amounts of unstructured data need to be stored and accessed quickly. They are also commonly used in big data and real-time data processing applications, where the speed of data processing is more important than data consistency.
Examples of popular NoSQL databases include MongoDB, Cassandra, Couchbase, and Redis.
In a document-based NoSQL database, data is stored in collections, which are similar to tables in a relational database. Each collection contains one or more documents, which are similar to records in a table. However, unlike relational databases, the structure of each document in a collection can be different, allowing for greater flexibility and scalability. Documents can be nested and contain arrays, making it easy to store complex data structures.
In a key-value NoSQL database, data is stored and retrieved using a unique key that identifies each piece of data. Each key-value pair is stored independently of other data, allowing for fast retrieval and storage of data.
NoSQL databases also typically provide features such as automatic sharding, which allows the database to scale horizontally across multiple servers, and eventual consistency, which allows for faster writes by not enforcing immediate consistency across all nodes in the database.
NoSQL databases are often used in applications where large amounts of unstructured or semi-structured data need to be stored and accessed quickly, such as web and mobile applications, big data processing, and real-time data analysis.
MongoDB documents are JSON-like objects, which consist of key-value pairs and can be nested. The keys represent the field names or attributes of the data, while the values represent the data itself.
MongoDB also includes the concept of a “database,” which is a logical container for collections. Each database can contain multiple collections, and each collection can have its own set of indexes and options.
In a MongoDB database, documents can have different fields and structures, and there is no need to define a fixed schema before storing data. This allows for more agility and faster development, as changes to the data model can be made more easily without requiring changes to the database schema or application code.
In contrast, SQL databases enforce a rigid structure, requiring the definition of a fixed schema before data can be stored. While this can provide more data consistency and integrity, it can also be more difficult to change the schema once data has been stored, requiring migrations and updates to application code.
Limited query functionality: NoSQL databases generally do not support the full range of SQL queries, which can make it more difficult to analyze data and perform complex joins across different collections.
Limited transaction support: NoSQL databases typically do not support transactions with the same level of data consistency and isolation as relational databases, which can lead to data inconsistencies and conflicts in some use cases.
Lack of standardization: Unlike SQL databases, which have a well-established standard for querying and managing data, NoSQL databases have a more fragmented landscape with different data models and APIs, which can make it harder to switch between databases or integrate with other systems.
Limited tooling and community support: NoSQL databases often have less mature tooling and a smaller community of developers compared to SQL databases, which can make it harder to find resources and support for development and maintenance.