With the development of social media, E-commerce, search, and the explosion of data, SQL was straining to keep up with all of the online requests, transactions, and activity. NoSQL is intended to handle large amounts of traffic and data. Yes, SQL was the first to arrive. It is used to exchange data with relational databases. Relational databases store information in a highly ordered, but also inflexible, manner. NoSQL, which derives its name from the phrase "not just SQL," makes it simpler to store several forms of data together. It is used to manage massive amounts of data because of its flexibility and, as a result, speed and scalability.
SQL databases dominated the development scene from the 1980s until the Internet age in the late 1990s. SQL was used to power large commercial systems, specialized goods, and bespoke applications of various kinds. However, the introduction of the Internet has fundamentally altered application development. The quantity of data, the structure of the data, the size of applications, and the manner in which applications have been built have all changed substantially. As a result of these shifts, numerous enterprises of all sizes have adopted NoSQL database technology.
Today, we have several possibilities, like MongoDB, Cassandra, Redis, Couchbase, DynamoDB, and Cosmos DB, which are increasing popularity, developing user communities, and rapidly releasing new features.
Many businesses have chosen NoSQL databases as a result of the surge of unstructured and semi-structured data. NoSQL stands for "non SQL" or "not just SQL." Although the terminology is a little hazy, it refers to a kind of database that is more adaptable and scalable than typical SQL databases. NoSQL databases aren't bound by the rigid schema structure of relational databases, and they're not limited to a specific data model as traditional databases are.
In truth, there's no rule that says a NoSQL database needs to follow a certain paradigm, and the sector is still young enough to consider any situation. Despite this, four fundamental NoSQL models have arisen, each with products:
Data is saved in a key-value structure that links unique identifiers to particular data blobs, allowing for the storage of almost any sort of data in any format. Redis, Berkeley DB, and Amazon DynamoDB are some examples of goods.
Document databases. Data is saved as documents in a format like JSON or XML, with each document having its own unique key and no restrictions on fields or contents. MongoDB, CouchDB, and BaseX are some examples of products.
Column-oriented databases. Data is stored as strongly-typed columns rather than rows, allowing for rapid querying and aggregation of massive amounts of data. These databases are also known as column-store, wide-column store, and column-family databases. HBase, Bigtable, and Cloudera are some examples of products.
Graph databases Data is kept in graph structures that specify how the data is related, thereby deeming the data's connections as significant as the data itself. Neo4J and InfiniteGraph are two instances of products. Depending on the workloads that a company is seeking to handle, each form of NoSQL database has benefits and limitations. Furthermore, not all NoSQL products fall cleanly into any of these classifications. Azure Cosmos DB, for example, has five APIs that make the service more generally. The SQL API, MongoDB API, Cassandra API, Gremlin API, and Table API are among them.
Any legitimate NoSQL database, regardless of product, should be able to grow horizontally and manage dispersed large data workloads while providing significantly more flexibility than SQL databases. Because they are not bound by fixed schema structures, NoSQL databases make it simpler for development teams to get started on their projects. However, NoSQL databases aren't as well-developed as relational databases, and thus can't always guarantee the same degree of data integrity.
So the main question is:
When to opt for a NoSQL database?
- You're always adding new features, methods, and data kinds.
It is impossible to forecast how the application will evolve over time and this is a big concern.
- Changing a data model in SQL is cumbersome and necessitates code modifications.
A significant amount of work is spent defining the data model since modifications will have an influence on all or most of the layers of the application.
- NoSQL provides a flexible schema design or no pre-defined schema
Iterative and adaptive data modeling is used. Changing the structure or schema has no effect on development cycles or application downtime.
- You are not concerned with data consistency, and complete data integrity is not your primary aim.
This is relevant to the SQL requirement for ACID compliance mentioned above. For example, on social networking sites, it isn't critical that everyone sees your new post at the same moment, implying that data consistency isn't a priority.
- You have a lot of data, a variety of data kinds, and your data requirements will only rise over time.
NoSQL makes it simple to store several forms of data together, without having to spend time specifying what sort of data you're storing ahead of time.
- Your data requirements must scale up and down
NoSQL offers considerably better flexibility and the ability to regulate expenses as your data requirements evolve.
Other use cases include:
- Flexible schema for semi-structured or unstructured data
- Pre-defined access pathways and query patterns are limited.
- There are no complicated queries, stored procedures, or views
- Transactions with a high speed
- Large amounts of data (in the Terabyte range) need rapid and economical scaling
- Distributed computing and storage required
- There are no use cases for data warehouses, analytics, or business intelligence.
Each circumstance should be assessed on its own merits, taking into consideration the nature of your data as well as your workload needs. Keep in mind, though, that you are not confined to one database type over another. To fulfill their various needs, several firms have installed both SQL and NoSQL database systems, allowing them to benefit from the best of both worlds. Keep in mind that database technology is always developing, with new factors being introduced to the mix on a regular basis.
The more you understand your data and the various database solutions, the more informed a decision you'll be able to make when selecting a database system, and the better you'll be able to handle your workloads in the future. Follow us on https://www.linkedin.com/company/bayshoreintel for more such informative posts.