This relationship ensures that there is no payment entry of an employee whose details are not present in the master Employee table. EmployeeID column which would be a primary key of Employee table would be present as a foreign key in the Payments table, thus connecting the two tables with the referential property. The SQL databases have a relational property where different tables are related to each other with foreign keys, primary keys. There can be hundreds of such collections inside a MongoDB database. A collection can consist of many documents in which data is stored in JSON format of key-value. In MongoDB, data is stored in collections that are analogous to MySQL tables. These tables, in turn, reside inside the databases. In MySQL, the data is stored in tables, where the column denotes the attribute and row represents a particular record. SQL up until this point was the standard for querying and analytics and was well known by developers. The accumulation of unstructured data was one big step in the direction of the Big Data era, but on the flip side, since the data stored was unstructured, it was not possible to query that data using SQL. MongoDB, for example, mainly supports unstructured Documents. These new databases needed to support this different type of data that was unstructured and not suitable for schemas data like key-value stores, documents, text, graphs, and wide columns. This is when NoSQL databases started arriving on the scene. Such unstructured data could not be mapped to table-like schemas properly and thus arose the need for a different class of databases to support such unstructured data. SQL relational databases were the only viable commercial data storage solution until the 2000s when the internet and web 2.0 boom started to generate a large amount of unstructured data. In a structured schema, data is saved in a row-column format known as a Table and can be retrieved using queries formatted in the Structured Query Language (SQL). The schema represents the design of the database to which the data should adhere to. SQL databases, also known as relational databases, were designed to store data that has a structured schema. Since it is free, it has become a very popular choice in demand for SQL databases. It was first launched in the year 1995 and is now managed by Oracle. MySQL is an open-source SQL relational database, which is used for storing structured data in a table-like format. It was first launched in the year 2009 and has since become one of the leading databases in NoSQL space. MongoDB belongs to the family of NoSQL databases which is used for storing unstructured documents in JSON format. MySQL analytics is extremely common and is thus a good reference point for looking at doing analytics on Mongo. But it could have been any other SQL database also like Oracle, MS SQL Server, PostgreSQL, etc for our comparison. MongoDB vs MySQL –Īs we discussed, we will compare MongoDB with MySQL which is a well-known SQL database and most of our audience will be familiar with it. In this article, we will do an in-depth comparison between MongoDB vs SQL databases, (to be precise MySQL database) and will also touch upon the important topic of how we can perform MongoDB analytics similar to the ease with which analytics are done on its SQL counterparts. Both worlds have their own advantages and disadvantages and are meant for different types of use cases. The former deals with chaotic unstructured data, while the latter works with organized structured data. MongoDB and SQL databases are two polar opposite sides of the backend world. You can also set up a 15-minute call with a member of our team to see if Knowi may be a good BI solution for your project. As you’re exploring, be sure to check out our MongoDB Analytics page and our MySQL Analytics page where you can start Knowi trials. I know you’re still evaluating the relative strengths of Mongo and SQL–and I hope that this article is helpful.
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