Today, there are plenty of databases that are being deployed in various industries, some of them are SQL and some of them are NoSQL. Either be an analyst and engineer or IT decision-maker, you might be very familiar with these Relational Database Management Systems (RDBMS) and the Structured Query Language (SQL). While such terms are referring to the old standard paradigm, today a variety of database systems can be light-headed.
Moreover, the increasing volume of unstructured data, availability of storage and processing power, and emerging analytic requirements have produced a deep interest in different technologies. Another alternative of traditional RDBMS, NoSQL, can be accounted for as an assortment of modern use cases.
In order to make informed decisions regarding which database should be used for a specific purpose, we should be aware of the basic differences between SQL and NoSQL and the conditions each is suitable for. This is the essence of this blog.
For a note, the traditional database is a SQL database system that manages the tabular relational model in order to interpret data and their relationship. On the other hand, the NoSQL database is the latest database that presents a method for data storage and retrieval.
(Must read: What is SQL?)
SQL vs NoSQL
SQL databases are primarily known as Relational Databases (RDBMS) whereas NoSQL databases are termed as a non-relational or distributed database. However, the main DIFFERENCES are;
As simple, accessible and extremely powerful SQL databases explain and modify data-based structured query language (SQL), it is most versatile, documented and deeply-implemented that makes it safe for great complex queries.
On the other hand, it is very restrictive and therefore confined the user to function with predefined tabular schemas in order to ascertain (organize and understand) the data structure before working on that.
In addition to that, the provided data must follow the same structure and demands for critical upfront preparation, i.e, a minor manipulation in the structure can make the entire system both subtle and disruptive.
Since NoSQL databases hold dynamic schemas that let depiction of the alternative structures, adjacent to each other, promote greater flexibility for unstructured data. Here, flexibility implies documents, having their own sole structure, can be formed without addressing the predefined structure list.
While adding new features or fields, there is more freedom and the syntax varies from databases to databases, so novel fields can be augmented as the user proceeds. Additionally, unlike SQL, NoSQL languages need the standard interface, so accordingly, more complicated queries become difficult to execute.
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SQL databases are scalable vertically, i.e., more power or load (CPU, RAM or SSD) can be added over a single server for improving performance and efficiency.
In general, SQL databases embrace the upper limit that can be achieved when further hardware improvements can no longer be made that yields in long downtime and much costs in shaping up models.
On the other side, NoSQL databases are scalable horizontally through which a user can manage more traffic by adding more servers in the SQL database.
Ultimately, NoSQL becomes extensive and more powerful and becomes a prior choice for generous and varied datasets. Horizontal scalability gives redundancy significantly as a user has many machines and no single failure point.
For all SQL databases, huge support is provided from the side of vendors, as well as, for an extensive level of implementation with SQL databases, many independent consultants could also assist us.
Apart from that, since SQL databases are giants, i,e, they embrace numerous users across the world and come with precise documentation, therefore, there are various walkthroughs and SQL cheatsheets for aiding newbies.
Some examples of SQL databases include PostgreSQL, MySQL, Oracle and Microsoft SQL Server.
MySQL Workbench, Source: mysql.com
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Besides that, for many of the NoSQL databases, instead of gaining much popularity and followers, we have to rely on the support of the community and external professionalists for configuring and implementing NoSQL implementations at a huge scale.
Moreover, a user may cope in order to find out quality educational materials, supporting documents, and many things.
Some examples of NoSQL are Redis, RavenDB, CouchDB, MongoDB, Neo4j, etc.
MongoDB development model and connectivity portrait,
SQL databases are table-based with rigid schemas, or simply they represent relational tabular data with rules of consistency and integrity wherein tables raw and columns depict records and attributes respectively and keys have relatively compelled logical connections.
Due to the same, these are the best fit for multi-raw transactional-based applications, for example, many accounting and legacy systems are designed over relational databases.
In some sense, NoSQL databases have a quite different structure, they use graph stores, key-value pairs,wide-column stores and document-oriented stores for storing their data in a well organized or structured manner. A user can get unlimited flexibility and can choose any type of database engine.
Examples and description of NoSQL databases structure that fit into four categories;
Document: JSON document- they save semi-structured data, basically the objects that incorporate their own relevant data which are entirely different from each other. Used by MongoDB and CouchDB
Key-value: Key-value pairs- they keep dictionaries that approach distinct objects with a unique key, assigned to each. Used by Redis and DynamoDB.
Wide-Column: Tables with rows and dynamic columns- these databases exchange raw-oriented RDBMSs that enable sufficient high-dimensional data storage and separate records with changing attributes. Examples are Cassandra and HBase.
Graph: Nodes and edges- these databases augment the relationship, or direct connections amid various objects, to documents that permit fast traversal of largely connected datasets. Examples are Neo4j and Amazon Neptune.
NoSQL data models, Source
(Related reading: Hadoop vs MongoDB)
Diverging on the relational and non-relational databases for schemas and data storage design, SQL databases adopt schemas and predefined tables that are easily accessible for users.
We can have primary keys, fields, and values through which we could make various tables as per need. Also, secondary keys can be inserted inside tables that make intricate correlations amid several tables. By adopting this mode of data storage, SQL makes easy and swift analysis of massive data combinedly.
As opposed to that, NoSQL databases enable us to make databases with dynamic schemas, simply, there is no need to make database models upfront, as we extend databases, recent models and mix data types can be added seamlessly.
SQL databases follow the ACID properties, that are;
Atomicity: It implies that all the transactions are either conducted at a time, or they don’t occur, or simply must succeed completely or fail. They can’t be partially-failed even if the system interrupts.
Consistency: At each step, the database completely follows invariants, ie. some rules that verify and prevent interruptions. If an error occurs, it makes sure the roll-back action takes place.
Isolation: It means each of the transactions happens independently and has not accessed to other transactions. It saves simultaneous transactions to affect each other. Each transaction must yield in the exact final state as if they run sequentially, however running parallelly.
Durability: It makes final/concluding transactions, even if the failure of systems unable to roll-back the impacts of fruitful transactions. Lyman, whatever be the changes made in the databases, via transactions on completion, are allocated to databases without missing any updates.
(Also read: SQL for data scientists)
On the other side, NoSQL databases comply with the CAP theorem;
Consistency: Each of the received requests must obtain recent outcomes or errors. Here a user is able to observe the same data, doesn’t matter through which machine he/she is connected, so at the same time, data must be reflected to all its models, no matter data is written to which node.
Availability: Each of the requests has non-error results and must be responded from the system, simply means, the user either reads or writes, he/she must get a response regardless of the failure of operation or how recent the result is.
Partition Tolerance: The operation of systems would not be intruded with any delays or losses amid various nodes. In simple terms, Partition tolerance ensures that the system should be functional even there is a partition in systems where partition happens when a node is unable to get messages from other nodes of the system.
The CAP theorem, Source: doc.microsoft.com
Speed and Usages
Both SQL and NoSQL equally performs well in some conditions, but when focusing on and handling complex queries and extensive and ever-changing datasets, we can notice some differences; as
SQL databases demand a normalized form of storage data for limiting data redundancy, it diminishes the storage amount that needs by the databases and secures the smooth upgrading of records. E.g. SQL database demands to manage two different sheets in order to keep the records of employees information, departmental information, administrative records and many more.
For example, in order to conduct complex queries, like joins in the database that consists of several tables, it has become quite tedious when the size of data turns out to be crucial. Here, NoSQL overcomes this problem.
Since storage capacity is not a concerning issue with NoSQL databases, so no matter if data is replicated or counterfeit for them. Under NoSQL databases, data is typically stored in an optimized mode for queries, this simply implies that a user can save data in a manner as he/she could need it after executing some queries. By this way, NoSQL databases eliminate the issue of joins and make conducting of queries much easier and faster.
For example, in some of the NoSQL database, we can directly store the whole information of employees and associated departments and would perform various queries.
The remarked property of SQL database, ACID property, makes them significant and of high importance in various domains, for example, in bank transactions where the money would be supervised perfectly.
Also, if the data is structured and non-changing then adopting SQL databases is a good preference instead of NoSQL databases. If massive data is available which is unstructured, then NoSQL would be a good fit.
Following is the summarized points of difference between SQL and NoSQL
Difference between SQL and NoSQL
When to use SQL and NoSQL
SQL is used when
To protect the integrity of the database via rendering ACID acquiescence. Also, due to its structured data, an individual won’t need to require any sort of joined system support for applying various types of data.
To present huge benefits for transactional data whose structure will not modify frequently and where data integrity is predominant. It’s also best-suited for rapid analytical queries.
NoSQL is used when
To store several data types combinedly and to scale up through spreading across multiple servers. For example, when an application is being developed, an individual can opt NoSQL that can speed up performance with its swift growth stage.
To give extra flexibility and scalability that contributes itself to fast development and iteration.
Every business will have its own preferences of databases depending on specific requirements, like the demand of NoSQL, today, doesn’t imply sticking to it for all needs. There might be numerous preferences and distinct requirements, to prefer, over other databases.
Hence, figuring out business requirements and identifying suitable databases can correctly give the integrated support for business requirements and development. However, selecting or recommending an appropriate database is a nontrivial exercise.
We hope this SQL vs. NoSQL discussion will remain a useful approach that helps practitioners in making informed decisions. For more learning, stay tuned with us.