You can’t get away from learning about databases in data science. In fact, we need to become quite familiar with how to handle databases, how to quickly execute queries, etc. as data science professionals. There’s just no way around it!
There are two things you should know – learn all you can about database management and then figure out how to efficiently go about it. Trust me, you will go a long way in the data science domain.
As a Data Engineer, you are bound to work with all kinds of databases, especially SQL and NoSQL. However, most of us already have some considerable experience with SQL databases. Where we falter is when we have to transition to NoSQL databases, and it can be a bit intimidating at first, to be honest – the beginning is always the hardest.
So, to flatten the obstacle for you, we will talk about some key differences between these two kinds of databases in this article. This will give you an overview of the two and will make it easier for you to begin your journey. Let’s begin!
SQL is Standard Query Language that aids in querying relational databases. Hence, these databases are also often referred to as SQL databases.
The major advantage of databases over normal file storage systems is that it reduces data redundancy to a large extent, facilitates sharing of data among various users, and ensures the security of data which may be of immense importance to an organization.
Each database contains multiple tables, containing data in the form of rows and columns. And each table is related to a number of other tables within the database.
NoSQL or Not only SQL came to the picture in the late 2000s. These are flexible, scalable, cost-efficient, and schema-less databases.
They were born out of the need to handle huge amounts of data we generate in today’s world, which comes in different varieties and generated at a high pace.
In comparison with SQL databases, they are of multiple types: document-based, key-value based, wide column-based, graph-based. Each has its own pros and cons.
Now let’s deep dive and look at some of the key differences between SQL and NoSQL databases.
SQL databases are relational databases that store data in multiple related tables. These tables are relations. Each relation is organized into rows and columns. Each row is a tuple and holds a record, and each column is an attribute for which each record usually holds a value.
Tables in the database are related using the SQL keys. The columns in the table hold a certain type of data. If a record contains data with any other data type, then the database will throw an error. Also, a record needs to contain the same number of values as the number of columns in the table or needs to provide a NULL value explicitly. The most popular examples of SQL databases are MySQL, PostgreSQL, and Oracle.
There are 4 types of NoSQL databases: document-based, key-value based, wide column-based, graph-based. These databases store data in JSON-like documents. Each document has a key-value format, which means the data is semi-structured. Even if there is a missing value within a document for a key, the database will not throw an error.
A popular example is MongoDB. These databases store data in key-value format. Both keys and values can be anything, from string to complex values. The keys are stored in efficient index structures and can quickly and uniquely locate the values. This makes them ideal for applications that require fast retrieval of data.
Amazon DynamoDB is an example of such databases. This database stores the data in records similar to any relational database but it has the ability to store very large numbers of dynamic columns. Meaning, the number of column values for rows can vary in such databases. It groups the columns logically into column families. Cassandra is a popular example. They use nodes to store data entities like places, products, etc. and edges to store the relationship between them.
