Data now includes a vast breadth of information and is gathered in a variety of ways, including user information, location data, sensor-generated data, social media feeds, and more. Big data, a vast collection of unstructured data, is currently the foundation of analysis for many mission-critical applications.
There are two methods for storing such vast amounts of data: either in relational databases or through distributed databases. SQL is the best option for the first approach, whereas MongoDB (NoSQL) is a good option for the second.
Since both databases have their own features. Do you want to know how SQL databases and NoSQL databases like MongoDB differ from one another? How do you pick the best one for your data? You can find the answers by using this article.
What is MongoDB?
MongoDB is a document-based, versatile, and scalable NoSQL database management system that uses key-value sets to store data and may support many data models. It was created as a way to deal with enormous amounts of dispersed data that relational models, which typically provide rows and tables, cannot handle well. MongoDB is free and open-source.
- It is a powerful query language that enables CRUD operations, text search, and aggregation functions.
- Due to embedded data models, MongoDB requires fewer input and output procedures than relational databases. MongoDB indexes also support faster queries.
- It creates replica datasets to enable fault tolerance. Data is kept on many servers using replication, which offers high availability and redundancy.
- Numerous storage engines ensure that the appropriate engine is used for each workload, improving performance.
What is a SQL database?
A SQL database, often known as a relational database, is made up of a number of highly structured tables where each row represents a certain type of data item and each column designates a particular field of data. Relational databases are built using a structured query language (SQL) to create, store, update, and retrieve data.
- For highly transactional, busy, and frequently used database systems, SQL offers high-performance programming capabilities.
- SQL is interoperable with a wide range of databases, including MS Access, Microsoft SQL Server, MySQL, Oracle Database, SAP HANA, SAP Adaptive Server, etc.
- Scalability and flexibility are provided via SQL. It is relatively simple to add new tables, and it is also possible to discard or delete any previously established or unused tables from a database.
Rather than comparing MongoDB vs SQL, one can take advantage of both the SQL and NoSQL databases through data virtualization. One of the best analytics tools that are completely built on data virtualization is Knowi. Knowi allows users to not only connect directly to MongoDB but also provides a high-level user interface to generate queries and manipulate data.
Users can create queries by utilizing native MongoDB queries or point-and-click software. The data can then be further altered by users using Cloud9QL, a SQL-based language. There is no need to manage an ETL process to distribute and store the data in a SQL database because everything is done in real-time on the active MongoDB server.
To put it another way, a Knowi user can use the application to filter, join, and conduct aggregation on real-time data from MongoDB, in the same way, without actually using a SQL database, they behave just as they would with one.
We went a step further and completely included this MongoDB query functionality into our business intelligence platform, allowing customers to conduct real-time data analysis, data science, and data visualization on top of the MongoDB data without any issues.
All in all, Knowi works as a REST API reporting tool that allows you to connect to any REST-enabled service, build visualizations and share and track dashboards quickly and easily. As a result, your firm may quickly transition from a MongoDB database to a business intelligence application.
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