MongoDB Insights

What is MongoDB?

MongoDB is a NoSQL document database. It stores data in flexible, JSON-like documents, meaning fields can vary from document to document and the data structure can be changed over time. It is commonly used for applications requiring high availability, scalability, and flexible schemas, such as content management systems, e-commerce platforms, and IoT applications.

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What other technologies are related to MongoDB?

MongoDB Competitor Technologies

PostgreSQL

PostgreSQL is a relational database system and competes with MongoDB in the broader database market, although they have different strengths and use cases.

mentioned alongside MongoDB in 23% (135.2k) of relevant job posts

MySQL

MySQL is a relational database system and a direct competitor to MongoDB, especially for applications requiring SQL and ACID transactions.

mentioned alongside MongoDB in 21% (137.9k) of relevant job posts

Cassandra

Cassandra is a NoSQL database that competes with MongoDB, particularly in scenarios requiring high availability and scalability.

mentioned alongside MongoDB in 48% (43.9k) of relevant job posts

Redis

Redis is an in-memory data store that competes with MongoDB in certain use cases like caching and session management, although MongoDB is more suitable for general-purpose data storage.

mentioned alongside MongoDB in 32% (61.5k) of relevant job posts

Elasticsearch

Elasticsearch is a search and analytics engine that competes with MongoDB in scenarios where full-text search and real-time analytics are important.

mentioned alongside MongoDB in 23% (45.4k) of relevant job posts

AWS DynamoDB

AWS DynamoDB is a NoSQL database service that competes with MongoDB in terms of scalability, performance, and use cases.

mentioned alongside MongoDB in 20% (24.8k) of relevant job posts

Microsoft SQL Server

Microsoft SQL Server is a relational database that competes with MongoDB in the broader database market.

mentioned alongside MongoDB in 8% (64.3k) of relevant job posts

Oracle

Oracle is a relational database system and competes with MongoDB in the broader database market.

mentioned alongside MongoDB in 7% (65.3k) of relevant job posts

MongoDB Complementary Technologies

Node.js

Node.js is a JavaScript runtime environment often used to build the backend for applications that use MongoDB as their database.

mentioned alongside MongoDB in 20% (109.2k) of relevant job posts

React

React is a JavaScript library for building user interfaces and is frequently used with backend applications that use MongoDB.

mentioned alongside MongoDB in 12% (119k) of relevant job posts

Docker

Docker is a containerization platform that simplifies deploying and managing MongoDB instances.

mentioned alongside MongoDB in 12% (122.5k) of relevant job posts

Which job functions mention MongoDB?

Job function

Jobs mentioning MongoDB

Orgs mentioning MongoDB

Engineering & R&D

3% (296.7k) job posts

↗

22 organizations

↗

Engineer

3% (295.8k) job posts

↗

3k organizations

↗

Software Engineer

6% (275.1k) job posts

Frontend Engineer

Data Engineer

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16% of Microservices Migration projects involve MongoDB

Microsoft

Principal Software Engineer

To develop a scalable cloud data warehouse that powers SQL analytics in Azure compute, utilizing technologies such as Azure Data Lake and Microsoft Fabric.

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JPMorgan Chase

CAO Intelligent Automation Program - Associate

To enhance operational efficiency by implementing intelligent automation technologies such as robotic process automation (RPA) and data transformation tools.

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Siemens

IT Solution Expert for SAP HCM Integration (m/f/d)

To integrate Workday with multiple SAP HCM systems using integration platforms like ShapeIn to streamline HR processes.

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Amazon

Applied Scientist, Q Developer Code Agent Science

Developing services that leverage generative AI and foundation models to improve the experience of developers using AWS tools and services.

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Walmart

(USA) Senior Manager, Automation Engineering - Data Engineer

To develop scalable data architectures and real-time data pipelines using technologies like Apache Airflow, Google Cloud Platform, and BigQuery to support AI/ML analytics.

Read more ↗