NoSQL, stands for "not only SQL," is a database design approach that provides flexible schemas for the storage and retrieval of data beyond the traditional table structures which are there in the relational databases.
If we look into the history, we will find that NoSQL databases have existed for many years but now it’s gradually gaining all the popularity in the world.
Why NoSQL?
Well, they are widely chosen today for their attributes around scale, performance, and ease of use. Think about Twitter, Facebook or Google, they all are collecting terabytes of user data every single day and NoSQL database makes it possible for users to organize such large quantities of data, it is widely used for Big data and real-time web applications.
NoSQL vs. SQL
The most important difference between a NoSQL and SQL is that a SQL database is a relational database. A relational database mostly stores data in tables, which are organized into columns. Each column contains one datatype (integer, real number, string, date etc.) and each row represents one of the table's instances. Non-relational databases do not store data in tables- instead there are multiple ways to store data such as Key-value, Document-based, Column-based, Graph based etc.
Key-value stores (Oracle NoSQL, Redis, Amazon Dynamo): For simple retrieval, group related data in collections with records identified by unique keys. While maintaining the benefits of NoSQL, key-value stores have just enough structure to mirror the value of relational databases.
Document databases (MongoDB and Couchbase): are primarily designed for storing data in the form of documents where the document can be a PDF, Microsoft word doc, XML or JSON file.
Wide-column databases (Google’s Bigtable, Cassandra, HBase): Rather than storing data in rows, this database stores it in grouped columns. They use keyspace concept, which is similar to the schema in a relational model.
Graph based (Infinite Graph, OrientDB, FlockDB): The relationships between the stored data points are defined using graph structures. Patterns in unstructured and semi-structured data can be identified using graph databases.
Pros and Cons of NoSQL
Pros:
There is no need for Separate Caching Layer
NoSQL databases don't need any dedicated high-performance server
It eliminates the need for a specific caching layer to store data
Big Data Capability, easy replication
Object-oriented programming which is very easy to use and flexible
It excels in multi-datacenter and distributed database operations
Highly Scalable at Low Cost
Open source development makes NoSQL software unique
Cons:
It Lacks standardization, Support and Maturity
Limited query capabilities
It Doesn't work as well with relational data
Relatively it has less community support from vendors as compared to relational databases
How NoSQL Databases Work?
(source- mongodb.com)
In the section mentioned above- NoSQL vs. SQL, I’ve mentioned ‘4 types of NoSQL database”- each has its own data model.
Each type of NoSQL database would be developed with a specific customer situation in mind, and there would be technical reasons for how each kind of database would be organized. The easiest type to describe is the document database, which would naturally incorporate both fundamental information and client information in a single JSON document. Each of the SQL column properties would be fields in this scenario, and the data values associated with each field would be the information of a customer's record.
For example: Last_name: "Learning", First_name: "Perfect", Middle_initial: "e", etc.
List of Common NoSQL Databases
Redis: Redis is an acronym for Remote Dictionary Server. It's written in the ANSI C programming language, licenced under the BSD licence, and known for its key-value store. It's a database that's free to use. It's an in-memory database with a persistent on-disk backup.
Amazon DynamoDB: It employs a NoSQL database model and supports documents, graphs, and columnar data types.
Couchbase: It's a document database for interactive web applications that uses NoSQL technology. It has a flexible data model, is scalable, and consistently delivers good performance.
MongoDB: It's a document-oriented NoSQL database that's open-source. It is written in C++ and uses JSON-like documents to transfer data. MongoDB is a widely used and scalable database.
Cassandra: It was created for inbox search at Facebook. It's a distributed data storage system that can handle a lot of structured data. Cassandra is a Java-based database. It has a SQL-like language called Cassandra Query Language (CQL) for querying the Cassandra Database.
Oracle NoSQL: It creates a mapping between user-defined keys and opaque data items.
Apache HBase: It is a distributed, non-relational Hadoop database created for Google's BigTable database and particularly suited for storing Big data.
RavenDB: It's a document database that uses NoSQL technology. Provides data integrity across various documents that is fully transactional (ACID).
Memcached: It is another well-known in-memory key-value database. It is again open-source and has high-performance.
Final Thoughts
We've seen that SQL and NoSQL databases perform nearly the same thing (store data), but in slightly different ways. As a result, selecting a database management system (DBMS) is a crucial and fundamental step in any data project. Of course, you can always choose one option and then change your mind afterwards. A little conceptual study and thought before the outset of a project, on the other hand, will save you time and money.
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