Plus, the databases are self-healing which means high availability.ĭocument databases are also highly scalable. However, some document database systems allow schema validation to be applied, should you want the other advantages of document databases but with a defined shape to the data.ĭocuments are considered individual units, which means they can be distributed across multiple servers. There is also no schema, meaning you can have flexibility should documents of different shapes be inserted.
The documents map nicely to objects in code in object-oriented programming languages, making it much easier to work with. There is no need to reference multiple documents or collections to view data of a single customer. The data is organized, and also easy to view. db.product.find()ĭue to documents being JSON-like, they are much easier to read and understand for a user.
In this query, the first argument (_id) represents the filter to use on the collection, and the second one-the projection-fields that should be returned by the query. How data is stored in a document non-relational databaseīelow is the query to get the product name and price of the given productid using the Mongo Query API (similar to SQL in the previous section). In this case, however, we are able to view all the data of one customer in a single place as a single document. The data is stored in pairs, similar to key/value pairs.Ĭonsider the same customer example as above. These types include strings numbers like int, float, and long dates objects arrays and even nested documents. This makes information organized and more structured.ĭocument databases store data in documents, which are usually JSON-like structures that support a variety of data types. The details of the product are in a separate product table. The order table in turn has product ids of the product items in the order. If someone needs more information on the order or address, they can query the matching order and address tables using an INNER JOIN operator with the id field. Here, the customer table stores the basic customer information, order id and address id. In a relational database, this would be stored in different tables with a key to join the tables when needed.ĭata is stored as tables with rows and columns in a relational database You have a variety of information that you store, like customer information, order information, and products. A simple example of a query is: SELECT PRODUCT_NAME, PRICE FROM PRODUCT WHERE PRODUCT _ID = 23 Developers can write SQL queries to perform CRUD (Create, Read, Update, Delete) operations. The most common way of interacting with relational database systems is using SQL (Structured Query Language). When one table’s primary key is used in another table, this column in the second table is known as the foreign key.
This column can then be used in other tables, if relationships are to be defined between them. Each table will have a column that must have unique values-known as the primary key. This is where a relational database gets its name from.Ī table uses columns to define the information being stored and rows for the actual data. Often, these tables have shared information between them, causing a relationship to form between tables. Most companies need both OLTP (operational) and OLAP (analytical) systems to store their data and can use a relational database, non-relational database, or both to serve their business purposes.Ī relational database, or relational database management system (RDMS), stores information in tables.
It includes data collected over a period of time and is usually stored in OLAP (Online Analytical Processing) systems, warehouses, or data lakes.ĭatabases are the most efficient way to permanently store and fetch operational and analytical data digitally.īased on their project requirements, companies need to choose a database that can: Such data is captured in real-time using Online Transaction Processing (OLTP) systems.Īnalytical data is used by businesses to find insights about customer behavior, product performance, and forecasting. Operational data is used for day-to-day transactions and needs to be fresh-for example, product inventory and bank balance. This article aims to address those questions by explaining what they are and how they differ, and to help you make an informed decision.ĭata of the digital age can be categorized into operational and analytical data. What type of database should be used? What’s the difference between relational and non-relational databases? When planning a new project or application, the discussion of database requirements will often come up.