SQL or No SQL?
Confused? Let’s find out what you need to know to make this important decision.
So, what is a SQL database?
SQL databases are primarily called as Relational Database Management System (RDBMS). They have a predefined schema in terms of tables and relationships. SQL databases represent data in the form of tables which consists of rows of data. SQL (Structured Query Language) is the standard programming language used to communicate and manipulate a relational database. Some examples are MySql, Oracle, Sqlite, Postgres and MS-SQL etc.
And, what is a No SQL database?
NoSQL databases are also called as non-relational or distributed database. NoSQL databases are a collection of key-value pairs, documents, graph databases or wide-column stores which do not have a standard or predefined schema. Some examples are MongoDB, BigTable, Redis, RavenDb, Cassandra, Hbase, Neo4j, CouchDb etc.
Why NoSQL databases are coming to the picture?
Now a days, we are dealing with huge amounts of data, data being organized and well-structured actually sometimes creates a problem, especially at extremely large volumes. The structured approach of RDBMS database like SQL slows down performance as data volume or size gets bigger and it is also not horizontally scalable to meet the needs of Big Data. That’s where No SQL databases come to rescue. But there are pros and cons of having a database with predefined schema and a database with no schema at all.
The following table of comparison will surely help you decide whether to go for SQL or No SQL.
SQL | NoSQL |
Tables with rows and predefined relations and schema | Documents, Key-Value pairs, graph databases or wide-column stores with no schema |
Vertically scalable | Horizontally Scalable |
SQL (Structured Query Language) is used for defining and manipulating data | Queries are focused on collection of documents. The syntax of these queries varies from database to database. |
Best fit for complex queries | Don’t have standard interfaces to perform complex queries, and the queries are not as powerful as SQL queries |
Not considered good for hierarchical data storage | Fits better for the hierarchical data storage because of the key-value pair way of storing data |
Best fit for heavy duty transactional type applications, as it is more stable and promises the atomicity as well as integrity of the data | Comparatively not that stable in high load and for complex transactional applications |
Excellent support available | User still have to rely on community support, and only limited outside experts are available |
SQL databases emphasizes on ACID properties (Atomicity, Consistency, Isolation and Durability) | NoSQL database follow the Brewers CAP theorem (Consistency, Availability and Partition tolerance) |
Here are some guidelines to help you decide
a. Based on type of data you have
- If your data is primarily structured – go for SQL.
- If your system is transaction-oriented like customer relationship management tools, accounting software, e-commerce platforms etc. – go for SQL
- If you need ACID compliance – go for SQL
- If your data requirements aren’t clear or if your data is unstructured – go for NoSQL
- If your system is more content oriented like article content, social media posts, sensor data etc. – go for NoSQL
- If you need CAP compliance – go for NoSQL
b. Based on ability to query
- If you need fast, efficient queries – go for SQL
- If you are ready to perform extra processing on the data while querying as a data-scientist does – go for NoSQL
c. Based on scaling
- If vertical scalability is not at higher priority and you are ready to increase the capacity of a single server (increasing CPU, RAM, or SSD) to scale your database – go for SQL
- If horizontal scalability at higher priority – go for NoSQL
Conclusion
SQL and NoSQL have been great inventions in keeping data storage and retrieval optimized and smooth. It is very hard to criticize one and completely go with the other option. Both are best in what they do and it is up to you to put them to better use depending on the business situations and needs. You’ll need to think about what your data looks like, how you’ll query your data, and the scalability you’ll need in the future. The aspects mentioned above are just thoughts and not hard and fast guidelines. These are written to help you decide. If you still have questions feel free to contact via comments.