Azure SQL Database vs MongoDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Azure SQL Database

Stacks585

Followers502

Votes13

Azure SQL Database vs MongoDB: What are the differences?

Introduction:

In today's digital landscape, databases are essential components for storing and managing vast amounts of data. Two popular options available are Azure SQL Database and MongoDB. While both serve the purpose of data storage, they have several key differences that set them apart.

Scalability: Azure SQL Database is a relational database management system (RDBMS) that follows a structured approach. It offers vertical and horizontal scalability, allowing users to increase storage size, performance, and throughput as per their requirements. On the other hand, MongoDB is a NoSQL database, providing horizontal scalability by allowing users to distribute data across multiple servers. It can handle large-scale data sets and high throughput by adding more commodity servers.
Data Model: Azure SQL Database uses a tabular data model with tables, columns, and rows similar to traditional SQL databases. It follows the relational database model, offering more rigid data structures, relationships, and support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. In contrast, MongoDB follows a flexible document model, storing data in JSON-like documents with dynamic schemas, allowing for more agile and unstructured data storage and retrieval. This schema-less approach offers more flexibility but may require additional data validation and consistency measures.
Query Language: Azure SQL Database uses Transact-SQL (T-SQL) as its query language, following the SQL (Structured Query Language) standard. This standardized language provides a rich and expressive set of operations for data querying and manipulation. On the other hand, MongoDB uses a query language inspired by JSON syntax. It supports a wide range of query operations, as well as advanced features like indexing, aggregation framework, and geospatial queries. MongoDB's query language is designed to work seamlessly with its document model.
Data Consistency: Azure SQL Database ensures strong data consistency by enforcing ACID transactions, ensuring that database modifications follow a set of predefined rules. This guarantees data integrity and avoids conflicts during concurrent operations. MongoDB, being a NoSQL database, provides eventual consistency by default. It allows for flexible and distributed data storage, where data changes propagate over time, enabling high availability and partition tolerance.
Integration and Ecosystem: Azure SQL Database is a part of the larger Microsoft Azure ecosystem, providing seamless integration with various Azure services like Azure App Service, Azure Functions, Azure Logic Apps, and Azure Data Factory. It offers built-in support for connecting with other Microsoft products and services. MongoDB, on the other hand, has a mature and thriving open-source community, providing extensive developer tooling, libraries, and frameworks for different programming languages and platforms.
Pricing Model: Azure SQL Database pricing is based on a combination of factors like compute power, storage size, and resource usage. It offers various pricing tiers to cater to different performance and budget requirements. MongoDB follows a different pricing model, offering a subscription-based licensing approach. Users need to consider factors like server deployment, data volume, and additional features to determine the overall cost.

In Summary, Azure SQL Database is a relational database management system known for its scalability, structured data model, Transact-SQL query language, strong data consistency, integration with the Azure ecosystem, and flexible pricing. MongoDB, on the other hand, is a NoSQL document database offering horizontal scalability, flexible data model, JSON-based query language, eventual consistency, a thriving open-source community, and subscription-based licensing.

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Manual

Advice on MongoDB, Azure SQL Database

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k

Comments

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

Comments

Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k

Comments

Detailed Comparison

MongoDB	Azure SQL Database
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.	It is the intelligent, scalable, cloud database service that provides the broadest SQL Server engine compatibility and up to a 212% return on investment. It is a database service that can quickly and efficiently scale to meet demand, is automatically highly available, and supports a variety of third party software.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	-
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 585
Followers 82.0K	Followers 502
Votes 4.1K	Votes 13
Pros & Cons
Pros 829 Document-oriented storage 594 No sql 554 Ease of use 465 Fast 410 High performance Cons 6 Very slowly for connected models that require joins 3 Not acid compliant 2 Proprietary query language	Pros 6 Managed 4 Secure 3 Scalable

What are some alternatives to MongoDB, Azure SQL Database?

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Amazon RDS

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Azure SQL Database vs MongoDB: What are the differences?

Introduction:

Scalability: Azure SQL Database is a relational database management system (RDBMS) that follows a structured approach. It offers vertical and horizontal scalability, allowing users to increase storage size, performance, and throughput as per their requirements. On the other hand, MongoDB is a NoSQL database, providing horizontal scalability by allowing users to distribute data across multiple servers. It can handle large-scale data sets and high throughput by adding more commodity servers.
Data Model: Azure SQL Database uses a tabular data model with tables, columns, and rows similar to traditional SQL databases. It follows the relational database model, offering more rigid data structures, relationships, and support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. In contrast, MongoDB follows a flexible document model, storing data in JSON-like documents with dynamic schemas, allowing for more agile and unstructured data storage and retrieval. This schema-less approach offers more flexibility but may require additional data validation and consistency measures.
Query Language: Azure SQL Database uses Transact-SQL (T-SQL) as its query language, following the SQL (Structured Query Language) standard. This standardized language provides a rich and expressive set of operations for data querying and manipulation. On the other hand, MongoDB uses a query language inspired by JSON syntax. It supports a wide range of query operations, as well as advanced features like indexing, aggregation framework, and geospatial queries. MongoDB's query language is designed to work seamlessly with its document model.
Data Consistency: Azure SQL Database ensures strong data consistency by enforcing ACID transactions, ensuring that database modifications follow a set of predefined rules. This guarantees data integrity and avoids conflicts during concurrent operations. MongoDB, being a NoSQL database, provides eventual consistency by default. It allows for flexible and distributed data storage, where data changes propagate over time, enabling high availability and partition tolerance.
Integration and Ecosystem: Azure SQL Database is a part of the larger Microsoft Azure ecosystem, providing seamless integration with various Azure services like Azure App Service, Azure Functions, Azure Logic Apps, and Azure Data Factory. It offers built-in support for connecting with other Microsoft products and services. MongoDB, on the other hand, has a mature and thriving open-source community, providing extensive developer tooling, libraries, and frameworks for different programming languages and platforms.
Pricing Model: Azure SQL Database pricing is based on a combination of factors like compute power, storage size, and resource usage. It offers various pricing tiers to cater to different performance and budget requirements. MongoDB follows a different pricing model, offering a subscription-based licensing approach. Users need to consider factors like server deployment, data volume, and additional features to determine the overall cost.

Azure SQL Database vs MongoDB

Overview